cloud computing cryptography research paper

Efficient Data Security Using Hybrid Cryptography on Cloud Computing

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• First Online: 25 September 2020
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• P. Chinnasamy 12 ,
• S. Padmavathi 12 ,
• R. Swathy 12 &
• S. Rakesh 12  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 145))

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Services are distributed among all servers and between the users and individuals in the cloud environment. Cloud providers have trouble guaranteeing file protection as security is the biggest issue in data handling and transfer as it can be accessed, misused and destroyed the original data form. Cloud security is a big concern in the cloud computing environment. To safeguard the cloud environment, many research works are being proposed. To overcome the security issue and achieve the CIA property (confidentiality, integrity and availability) the cryptography is used. Cryptography is the most useful technique to ensure a high level of data transfer and storage security. In traditional symmetric and asymmetric has some limitations. To solve this we are going to introducing a new hybrid technique to achieve high data security and confidentiality. In this article, we are combing ECC and Blowfish to implement a hybrid algorithm. The performance of the hybrid system is compared with the existing hybrid method and shows that the proposed method provides high security and confidentiality of patient data. The hybrid cryptography is used to defeat the inconveniences of both symmetric and asymmetric.

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Al-Shabi MA (2019) A survey on symmetric and asymmetric cryptography algorithms in information security. Int J Sci Res Pub 9(3). http://dx.doi.org/10.29322/IJSRP.9.03.2019.p.8779

Ngwe TT, Phyo SW (2015) Hybrid cryptosystem for data security. Int J Adv Electron Comput Sci 2(6)

Google Scholar  

Schenier on security. https://www.schneier.com/academic/blowfish/ . Last accessed 31 Oct 2017

Vasundhara S (2017) The advantages of elliptic curve cryptography for security. Glob J Pure Appl Math 13(9):4995–5011. ISSN 0973-1768

Kamara S, Lauter K (2010) Cryptographic cloud storage. Lect Notes Comput Sci 6054:136–149

Bansal VP, Singh S (205) A hybrid data encryption technique using RSA and blowfish for cloud computing on FPGAs. In: 2nd international conference on recent advances in engineering computational sciences (RAECS), Chandigarh, pp 1–5

Maitri PV, Verma A (2016) Secure file storage in cloud computing using hybrid cryptography algorithm. In: International conference on wireless communications, signal processing and networking (WiSPNET), Chennai, pp 1635–1638

Chinnasamy P, Deepalakshmi P (2018) Design of secure storage for health-care cloud using hybrid cryptography. In: 2nd international conference on inventive communication and computational technologies (ICICCT 2018). IEEE Xplore Compliant-Part number: CFP18BAC-ART; ISBN 978-1-5386-1974-2

Wang C, Cao N, Li J, Ren K, Lou W (2010) Secure ranked keyword search over encrypted cloud data. J ACM 43(3):431–473

Liang C, Ye N, Malekian R, Wang R (2016) The hybrid encryption algorithm of lightweight data in cloud storage. In: 2nd international symposium on agent, multi-agent systems and robotics (ISAMSR), Bangi, Malaysia, pp 160–166

Gansen Z, Chunming R, Jin L, Feng Z, Yong T (2010) Trusted data sharing over untrusted cloud storage providers. In: Proceedings of the 2nd IEEE international conference on cloud computing technology and science (CloudCom), pp 97–10

Agarwal A, Agarwal A (2011) The security risks associated with cloud computing. Int J Comput Appl Eng Sci I(CNS). ISSN 2231-4946

Dubey AK, Dubey AK, Namdev M, Shrivastava SS (2012) Cloud-user security based on RSA and MD5 algorithm for resource attestation and sharing in Java environment. In: CSI sixth international conference, software engineering (CONSEG)

Sarkar MK, Kumar S (2016) Ensuring data storage security in cloud computing based on hybrid encryption schemes. In: Fourth international conference on parallel, distributed and grid computing (PDGC), Waknaghat, pp 320–325. https://doi.org/10.1109/pdgc.2016.7913169

Chinnasamy P, Deepalakshmi P (2018) A scalable multilabel-based access control as a service for the cloud (SMBACaaS). Trans Emerg Telecommun Technol 29(8):e3458. https://doi.org/10.1002/ett.3458,2018

Yong P, Wei Z, Feng X, Zhong-hua D, Yang G, Dongqing C (2012) A secure cloud storage based on cryptographic techniques. J China Univ Posts Telecommun 19:182–189

Singh N, Kaur PD (2015) A hybrid approach for encrypting data on cloud to prevent DoS attacks. Int J Database Theor Appl 8(3):145–154. http://dx.doi.org/10.14257/ijdta.2015.8.3.12

Akomolafe OP, Abodunrin MO (2017) A hybrid cryptographic model for data storage in mobile cloud computing. Int J Comput Netw Inform Sec 6:53–60

Karthik, Chinnasamy, Deepalakshmi (2017) Hybrid cryptographic technique using OTP:RSA. In: 2017 IEEE international conference on intelligent techniques in control, optimization and signal processing (INCOS), Srivilliputhur, pp 1–4

Rahmani H, Sundararajan E, Zulkarnain Md, Ali AMZ (2013) Encryption as a service (EaaS) as a solution for cryptography in cloud. Procedia Technol 11:1202–1210

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Assistant Professor, Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, 641062, India

P. Chinnasamy, S. Padmavathi, R. Swathy & S. Rakesh

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Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

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Department of Electrical Engineering, Dayeh University, Changhua, Taiwan

Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

Álvaro Rocha

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Chinnasamy, P., Padmavathi, S., Swathy, R., Rakesh, S. (2021). Efficient Data Security Using Hybrid Cryptography on Cloud Computing. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 145. Springer, Singapore. https://doi.org/10.1007/978-981-15-7345-3_46

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Security Issues and Use of Cryptography in Cloud Computing

• J. Kaur , Rajbhupinder Kaur
• Published 2014
• Computer Science, Engineering

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An authenticate model of cloud interaction using cryptography, enhancing information security in cloud computing environment using cryptographic techniques.

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Cloud Cryptography: User End Encryption

A review paper of data security in cloud computing, securing cloud computing environment using quantum key distribution, ensuring security for data storage in cloud computing using hecc- elgamal cryptosystem and gso optimization, pro cryptography and cryptanalysis: creating advanced algorithms with c# and .net, 16 references, analyzing data security for cloud computing using cryptographic algorithms, file security in cloud using two-tier encryption and decryption, use of cryptography in cloud computing, a newer user authentication, file encryption and distributed server based cloud computing security architecture, security issues and solutions in cloud computing, cloud computing: security issues and research challenges, cloud-user security based on rsa and md5 algorithm for resource attestation and sharing in java environment, enhanced survey and proposal to secure the data in cloud computing environment, understanding cloud computing vulnerabilities, hybrid algorithm with dsa , rsa and md 5 encryption algorithm for wireless devices, related papers.

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How a scientist’s lifelong love of puzzles led to cryptography that could help quantum-proof the world

For Vadim Lyubashevsky, the journey into securing the world’s systems from tomorrow’s quantum risks started with math puzzles.

At six years old and growing up in Kyiv, Ukraine, Lyubashevsky saw his grandfather more often than he saw his parents, who both worked. His granddad, a math teacher, had a special love for chess and solving number puzzles. With all their time together, that passion passed on to him.

When he was nine, Lyubashevsky moved with his parents to the US. He’s now a cryptographer at IBM Research and one of the leading minds behind some of the quantum-safe algorithms the US government has selected to replace the current global encryption standards. The inspiration for his career trajectory, he said, came from the math games he used to play with his grandfather. The complex, far-reaching equations that led him to work on cryptography could soon help secure the world’s most sensitive data.

“It’s not that there’s something wrong with the type of cryptography we use today,” said Lyubashevsky, who now lives in Zug, Switzerland, with his children. “It’s just that we will soon have technology that can crack it, which we didn’t have back when RSA-based encryption was developed. That technology is quantum computers.”

RSA is a type of asymmetric encryption, which uses public and private keys to secure our sensitive data. That includes anything from medical records and bank documents to secure website access codes and email passwords. It was first outlined in 1977, when scientists Ron Rivest, Adi Shamir, and Leonard Adleman publicly described their RSA algorithm, which takes its name from the first letters of their surnames.

The RSA standard still underpins many of the popular encryption systems today. But quantum computers have been maturing at breakneck speed over the past decade. These machines rely on the mathematics of the quantum world and Why it’s time to take quantum-safe cryptography seriously. Quantum-safe cryptography is here. Read more about why it’s time for industry to adopt it . researchers estimate they may soon be able to decrypt most of our data that has been secured through RSA encryption and other contemporary techniques. There’s an impending need for a totally new type of encryption.

Lyubashevsky had not yet been born when RSA was unveiled. But even with his love of puzzles and mathematics, he didn’t study RSA. Instead, in the early 2000s as a PhD student at the University of California, San Diego, he dove into lattice-based cryptography, a type of encryption method that was very niche at the time.

The beauty of equations

In the intervening years, many have come to believe that lattice-based cryptography will be the main way we protect sensitive data from future quantum computers.

Cryptography is a science — but Lyubashevsky also sees it as something like art. It’s not something that naturally existed — we created it. “If there were no Mozart, none of the beautiful things that he composed would exist,” Lyubashevsky said. “Whereas if there were no Einstein, relativity would still be here, and we eventually would have discovered it. Cryptography is more like the former — the world would go on well-enough without concepts like public key encryption and zero-knowledge proofs ever existing, but it is much better with them in it.“

People have been encrypting things with increasingly complex ciphers for millennia, from Greek scytales to the Enigma codes cracked by Alan Turing in World War II. In 1973, the US National Bureau of Standards (which later became NIST, the National Institute of Standards and Technology) asked the world’s cryptographers to develop a block cipher to use as a national standard.

At IBM, a dedicated cryptography team led by Horst Feistel designed a cipher called Lucifer , which won the competition and became DES, or Data Encryption Standard. DES was cracked in 1997 , mainly due to the small size of the encryption key, and computers of the time being able to find a solution with brute-force computation. This led NIST to look for a new standard, and in 2000, the Rijndael cypher led to AES or the Advanced Encryption Standard, which is what many systems are secured with today.

AES is incredibly secure — many consider it to be quantum-proof. IBM researchers expect that a quantum computer built by 2030 would take a hundred billion years to break the AES-128 version of the standard. But AES serves a different purpose to RSA, and the two are not interchangeable. AES assumes the communicating parties share a secret key.

The goal of RSA, on the other hand, is to allow two parties, who do not initially share any common secret, to create a secret that only they share. This secret key then can be used by AES. The security of RSA hinges on the hardness of factoring large numbers. While it’s easy to factor a small number like 12 (3x4), take a large number and even the most advanced supercomputer will stumble. It would take some 300 trillion years for today’s best classical machines to break an RSA-based 2048-bit encryption key.

But in theory, a quantum computer should be able to factor any large number considerably quicker. The same quantum computer that would struggle with AES should be able to break RSA-2048 in just a few hours. This is where lattice-based cryptography comes in.

From numbers to vectors

Recently, Lyubashevsky’s lattice research has set the security world abuzz, but that wasn’t always that the case. The research community had known since the 1990s that a future quantum computer should be able to break RSA, thanks to Shor’s algorithm . But physical quantum computers at the time were in their infancy. Quantum-safe cryptography, Lyubashevsky said, “was not really on many people’s radar.” He chose to do his PhD in lattice-based cryptography precisely because it wasn’t mainstream, mesmerized by the beauty of cryptographic equations. “I could just sit there by myself and just work on this math for, you know, years.”

Having finished his PhD in 2008, Lyubashevsky was offered a postdoc position at Tel Aviv University. He jumped on the opportunity as Israel is a leader in the study of modern cryptography. Tel Aviv university is also the alma mater of Adi Shamir, one of the original RSA developers. But it was Lyubashevsky’s post-doc advisor, Oded Regev, a theoretical computer scientist now at New York University’s Courant Institute of Mathematical Sciences, who drew him to the university. Regev was instrumental in creating the foundations of lattice cryptography and made the connection between quantum and lattices.

If you picture a two-dimensional lattice and pick a point, it's fairly intuitive for someone to find the closest point to it. But with a lattice with hundreds of dimensions, it’s very difficult, as you would have to try out many combinations to find the next closest point. The security of lattice cryptography is based on the believed hardness, even against attackers possessing a quantum computer, of such problems. Lyubashevsky’s two years in Israel, working closely with so many world-leading cryptographers, led to him thinking more and more about the possible practical applications of lattices, particularly how they could help reduce quantum risk.

The applications became even clearer after he left traditional academia, first heading to Inria (the French national research institute for digital science and technology) in 2010, and then to IBM Research in Zurich five years later. He moved for personal reasons, he said, but also because he had visited IBM a few times before and moving just made sense. At IBM, “it all started to become very practical very fast — in leaps and bounds, where lattice-based cryptography was really increasing in terms of potential utility,” Lyubashevsky said.

NIST and the lattice frenzy

A year after Lyubashevsky joined IBM, there was a global call from NIST to submit proposals for new algorithms that would be safe against future quantum computers. He focused all his attention on the problem. “This was now the real world, I realized that it was time to dot the i’s and cross the t's,” he said. “Otherwise, what was the point of all that theory? I just had to do it.”

The team, based in Zurich, proposed three schemes:

• CRYSTALS-Kyber public-key encryption,
• CRYSTALS-Dilithium digital signature algorithm, and
• FALCON digital signature algorithm.

Cryptographers from all over the world submitted dozens of cryptographic schemes for potential standardization, and in 2020, Read more about how IBM scientists helped develop NIST’s quantum-safe standards . NIST picked the winners . CRYSTALS-Kyber won for general encryption, used in cases like accessing secure websites, for example. This algorithm has small encryption keys and ciphertexts that two communicating parties can exchange easily. For digital signatures, NIST chose CRYSTALS-Dilithium, FALCON and SPHINCS+. Out of those, Lyubashevsky and his IBM colleague, Gregor Seiler, worked on developing the first three, while IBM researcher Ward Beullens contributed to SPHINCS+ before joining IBM.

The four algorithms will likely be published as formal standards this year and are believed to be extremely tough to break — now, or as many believe, pretty much ever. “To break the smallest version of Kyber with a computer today, you’d need the memory the size of a small moon,” said Michael Osborne, CTO of IBM’s quantum-safe security research. “That is just the incomprehensible amount of energy, and of compute resources.”

The news about the success of the IBM algorithms with NIST was encouraging, Lyubashevsky said, but he knew this was just the beginning. Now he and his colleagues had to get companies and organizations to switch to these new algorithms — the sooner the better.

In May 2022, the Biden administration issued a National Security Memorandum , outlining how US agencies will migrate to new, quantum-resistant algorithms. Shortly after, the Quantum Computing Cybersecurity Preparedness Act passed by Congress, mandated federal agencies to prepare an inventory of items for the transition to the new standards. Across the Atlantic, policymakers at the European Commission have been discussing recommendations for quantum-safe migration. A recent paper outlines the need for a new EU coordinated action plan to ensure companies across the continent adopt quantum-secured technologies as soon as possible.

While NIST finalizes the standards, Lyubashevsky continues to work on new algorithms. Although math puzzles are what led him to his professional passion, he’s no longer interested in solving them on paper. “Now, I want to help solve real world problems instead,” he said.

• Katia Moskvitch
• Cryptography
• Quantum Safe
• Note 1 :  Why it’s time to take quantum-safe cryptography seriously. Quantum-safe cryptography is here. Read more about why it’s time for industry to adopt it .   ↩︎
• Note 2 :  Read more about how IBM scientists helped develop NIST’s quantum-safe standards .   ↩︎

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Computer Science > Cryptography and Security

Title: cyber protection applications of quantum computing: a review.

Abstract: Quantum computing is a cutting-edge field of information technology that harnesses the principles of quantum mechanics to perform computations. It has major implications for the cyber security industry. Existing cyber protection applications are working well, but there are still challenges and vulnerabilities in computer networks. Sometimes data and privacy are also compromised. These complications lead to research questions asking what kind of cyber protection applications of quantum computing are there and what potential methods or techniques can be used for cyber protection? These questions will reveal how much power quantum computing has and to what extent it can outperform the conventional computing systems. This scoping review was conducted by considering 815 papers. It showed the possibilities that can be achievedif quantum technologies are implemented in cyber environments. This scoping review discusses various domains such as algorithms and applications, bioinformatics, cloud and edge computing, the organization of complex systems, application areas focused on security and threats, and the broader quantum computing ecosystem. In each of these areas, there is significant scope for quantum computing to be implemented and to revolutionize the working environment. Numerous quantum computing applications for cyber protection and a number of techniques to protect our data and privacy were identified. The results are not limited to network security but also include data security. This paper also discusses societal aspects, e.g., the applications of quantum computing in the social sciences. This scoping review discusses how to enhance the efficiency and security of quantum computing in various cyber security domains. Additionally, it encourages the reader to think about what kind of techniques and methods can be deployed to secure the cyber world.

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Private Cloud Compute: A new frontier for AI privacy in the cloud

Apple Intelligence is the personal intelligence system that brings powerful generative models to iPhone, iPad, and Mac. For advanced features that need to reason over complex data with larger foundation models , we created Private Cloud Compute (PCC), a groundbreaking cloud intelligence system designed specifically for private AI processing. For the first time ever, Private Cloud Compute extends the industry-leading security and privacy of Apple devices into the cloud, making sure that personal user data sent to PCC isn’t accessible to anyone other than the user — not even to Apple. Built with custom Apple silicon and a hardened operating system designed for privacy, we believe PCC is the most advanced security architecture ever deployed for cloud AI compute at scale.

Apple has long championed on-device processing as the cornerstone for the security and privacy of user data. Data that exists only on user devices is by definition disaggregated and not subject to any centralized point of attack. When Apple is responsible for user data in the cloud, we protect it with state-of-the-art security in our services — and for the most sensitive data, we believe end-to-end encryption is our most powerful defense . For cloud services where end-to-end encryption is not appropriate, we strive to process user data ephemerally or under uncorrelated randomized identifiers that obscure the user’s identity.

Secure and private AI processing in the cloud poses a formidable new challenge. Powerful AI hardware in the data center can fulfill a user’s request with large, complex machine learning models — but it requires unencrypted access to the user's request and accompanying personal data. That precludes the use of end-to-end encryption, so cloud AI applications have to date employed traditional approaches to cloud security. Such approaches present a few key challenges:

• Cloud AI security and privacy guarantees are difficult to verify and enforce. If a cloud AI service states that it does not log certain user data, there is generally no way for security researchers to verify this promise — and often no way for the service provider to durably enforce it. For example, a new version of the AI service may introduce additional routine logging that inadvertently logs sensitive user data without any way for a researcher to detect this. Similarly, a perimeter load balancer that terminates TLS may end up logging thousands of user requests wholesale during a troubleshooting session.
• It’s difficult to provide runtime transparency for AI in the cloud. Cloud AI services are opaque: providers do not typically specify details of the software stack they are using to run their services, and those details are often considered proprietary. Even if a cloud AI service relied only on open source software, which is inspectable by security researchers, there is no widely deployed way for a user device (or browser) to confirm that the service it’s connecting to is running an unmodified version of the software that it purports to run, or to detect that the software running on the service has changed.
• It’s challenging for cloud AI environments to enforce strong limits to privileged access. Cloud AI services are complex and expensive to run at scale, and their runtime performance and other operational metrics are constantly monitored and investigated by site reliability engineers and other administrative staff at the cloud service provider. During outages and other severe incidents, these administrators can generally make use of highly privileged access to the service, such as via SSH and equivalent remote shell interfaces. Though access controls for these privileged, break-glass interfaces may be well-designed, it’s exceptionally difficult to place enforceable limits on them while they’re in active use. For example, a service administrator who is trying to back up data from a live server during an outage could inadvertently copy sensitive user data in the process. More perniciously, criminals such as ransomware operators routinely strive to compromise service administrator credentials precisely to take advantage of privileged access interfaces and make away with user data.

When on-device computation with Apple devices such as iPhone and Mac is possible, the security and privacy advantages are clear: users control their own devices, researchers can inspect both hardware and software, runtime transparency is cryptographically assured through Secure Boot, and Apple retains no privileged access (as a concrete example, the Data Protection file encryption system cryptographically prevents Apple from disabling or guessing the passcode of a given iPhone).

However, to process more sophisticated requests, Apple Intelligence needs to be able to enlist help from larger, more complex models in the cloud. For these cloud requests to live up to the security and privacy guarantees that our users expect from our devices, the traditional cloud service security model isn't a viable starting point. Instead, we need to bring our industry-leading device security model, for the first time ever, to the cloud.

The rest of this post is an initial technical overview of Private Cloud Compute, to be followed by a deep dive after PCC becomes available in beta. We know researchers will have many detailed questions, and we look forward to answering more of them in our follow-up post.

Designing Private Cloud Compute

We set out to build Private Cloud Compute with a set of core requirements:

• Stateless computation on personal user data. Private Cloud Compute must use the personal user data that it receives exclusively for the purpose of fulfilling the user’s request. This data must never be available to anyone other than the user, not even to Apple staff, not even during active processing. And this data must not be retained, including via logging or for debugging, after the response is returned to the user. In other words, we want a strong form of stateless data processing where personal data leaves no trace in the PCC system.
• Enforceable guarantees. Security and privacy guarantees are strongest when they are entirely technically enforceable, which means it must be possible to constrain and analyze all the components that critically contribute to the guarantees of the overall Private Cloud Compute system. To use our example from earlier, it’s very difficult to reason about what a TLS-terminating load balancer may do with user data during a debugging session. Therefore, PCC must not depend on such external components for its core security and privacy guarantees. Similarly, operational requirements such as collecting server metrics and error logs must be supported with mechanisms that do not undermine privacy protections.
• No privileged runtime access. Private Cloud Compute must not contain privileged interfaces that would enable Apple’s site reliability staff to bypass PCC privacy guarantees, even when working to resolve an outage or other severe incident. This also means that PCC must not support a mechanism by which the privileged access envelope could be enlarged at runtime, such as by loading additional software.
• Non-targetability. An attacker should not be able to attempt to compromise personal data that belongs to specific, targeted Private Cloud Compute users without attempting a broad compromise of the entire PCC system. This must hold true even for exceptionally sophisticated attackers who can attempt physical attacks on PCC nodes in the supply chain or attempt to obtain malicious access to PCC data centers. In other words, a limited PCC compromise must not allow the attacker to steer requests from specific users to compromised nodes; targeting users should require a wide attack that’s likely to be detected. To understand this more intuitively, contrast it with a traditional cloud service design where every application server is provisioned with database credentials for the entire application database, so a compromise of a single application server is sufficient to access any user’s data, even if that user doesn’t have any active sessions with the compromised application server.
• Verifiable transparency. Security researchers need to be able to verify, with a high degree of confidence, that our privacy and security guarantees for Private Cloud Compute match our public promises. We already have an earlier requirement for our guarantees to be enforceable. Hypothetically, then, if security researchers had sufficient access to the system, they would be able to verify the guarantees. But this last requirement, verifiable transparency, goes one step further and does away with the hypothetical: security researchers must be able to verify the security and privacy guarantees of Private Cloud Compute, and they must be able to verify that the software that’s running in the PCC production environment is the same as the software they inspected when verifying the guarantees.

This is an extraordinary set of requirements, and one that we believe represents a generational leap over any traditional cloud service security model.

Introducing Private Cloud Compute nodes

The root of trust for Private Cloud Compute is our compute node: custom-built server hardware that brings the power and security of Apple silicon to the data center, with the same hardware security technologies used in iPhone, including the Secure Enclave and Secure Boot . We paired this hardware with a new operating system: a hardened subset of the foundations of iOS and macOS tailored to support Large Language Model (LLM) inference workloads while presenting an extremely narrow attack surface. This allows us to take advantage of iOS security technologies such as Code Signing and sandboxing .

On top of this foundation, we built a custom set of cloud extensions with privacy in mind. We excluded components that are traditionally critical to data center administration, such as remote shells and system introspection and observability tools. We replaced those general-purpose software components with components that are purpose-built to deterministically provide only a small, restricted set of operational metrics to SRE staff. And finally, we used Swift on Server to build a new Machine Learning stack specifically for hosting our cloud-based foundation model .

Let’s take another look at our core Private Cloud Compute requirements and the features we built to achieve them.

Stateless computation and enforceable guarantees

With services that are end-to-end encrypted, such as iMessage, the service operator cannot access the data that transits through the system. One of the key reasons such designs can assure privacy is specifically because they prevent the service from performing computations on user data. Since Private Cloud Compute needs to be able to access the data in the user’s request to allow a large foundation model to fulfill it, complete end-to-end encryption is not an option. Instead, the PCC compute node must have technical enforcement for the privacy of user data during processing, and must be incapable of retaining user data after its duty cycle is complete.

We designed Private Cloud Compute to make several guarantees about the way it handles user data:

• A user’s device sends data to PCC for the sole, exclusive purpose of fulfilling the user’s inference request. PCC uses that data only to perform the operations requested by the user.
• User data stays on the PCC nodes that are processing the request only until the response is returned. PCC deletes the user’s data after fulfilling the request, and no user data is retained in any form after the response is returned.
• User data is never available to Apple — even to staff with administrative access to the production service or hardware.

When Apple Intelligence needs to draw on Private Cloud Compute, it constructs a request — consisting of the prompt, plus the desired model and inferencing parameters — that will serve as input to the cloud model. The PCC client on the user’s device then encrypts this request directly to the public keys of the PCC nodes that it has first confirmed are valid and cryptographically certified. This provides end-to-end encryption from the user’s device to the validated PCC nodes, ensuring the request cannot be accessed in transit by anything outside those highly protected PCC nodes. Supporting data center services, such as load balancers and privacy gateways, run outside of this trust boundary and do not have the keys required to decrypt the user’s request, thus contributing to our enforceable guarantees.

Next, we must protect the integrity of the PCC node and prevent any tampering with the keys used by PCC to decrypt user requests. The system uses Secure Boot and Code Signing for an enforceable guarantee that only authorized and cryptographically measured code is executable on the node. All code that can run on the node must be part of a trust cache that has been signed by Apple, approved for that specific PCC node, and loaded by the Secure Enclave such that it cannot be changed or amended at runtime. This also ensures that JIT mappings cannot be created, preventing compilation or injection of new code at runtime. Additionally, all code and model assets use the same integrity protection that powers the Signed System Volume . Finally, the Secure Enclave provides an enforceable guarantee that the keys that are used to decrypt requests cannot be duplicated or extracted.

The Private Cloud Compute software stack is designed to ensure that user data is not leaked outside the trust boundary or retained once a request is complete, even in the presence of implementation errors. The Secure Enclave randomizes the data volume’s encryption keys on every reboot and does not persist these random keys , ensuring that data written to the data volume cannot be retained across reboot. In other words, there is an enforceable guarantee that the data volume is cryptographically erased every time the PCC node’s Secure Enclave Processor reboots. The inference process on the PCC node deletes data associated with a request upon completion, and the address spaces that are used to handle user data are periodically recycled to limit the impact of any data that may have been unexpectedly retained in memory.

Finally, for our enforceable guarantees to be meaningful, we also need to protect against exploitation that could bypass these guarantees. Technologies such as Pointer Authentication Codes and sandboxing act to resist such exploitation and limit an attacker’s horizontal movement within the PCC node. The inference control and dispatch layers are written in Swift, ensuring memory safety, and use separate address spaces to isolate initial processing of requests. This combination of memory safety and the principle of least privilege removes entire classes of attacks on the inference stack itself and limits the level of control and capability that a successful attack can obtain.

No privileged runtime access

We designed Private Cloud Compute to ensure that privileged access doesn’t allow anyone to bypass our stateless computation guarantees.

First, we intentionally did not include remote shell or interactive debugging mechanisms on the PCC node. Our Code Signing machinery prevents such mechanisms from loading additional code, but this sort of open-ended access would provide a broad attack surface to subvert the system’s security or privacy. Beyond simply not including a shell, remote or otherwise, PCC nodes cannot enable Developer Mode and do not include the tools needed by debugging workflows.

Next, we built the system’s observability and management tooling with privacy safeguards that are designed to prevent user data from being exposed. For example, the system doesn’t even include a general-purpose logging mechanism. Instead, only pre-specified, structured, and audited logs and metrics can leave the node, and multiple independent layers of review help prevent user data from accidentally being exposed through these mechanisms. With traditional cloud AI services, such mechanisms might allow someone with privileged access to observe or collect user data.

Together, these techniques provide enforceable guarantees that only specifically designated code has access to user data and that user data cannot leak outside the PCC node during system administration.

Non-targetability

Our threat model for Private Cloud Compute includes an attacker with physical access to a compute node and a high level of sophistication — that is, an attacker who has the resources and expertise to subvert some of the hardware security properties of the system and potentially extract data that is being actively processed by a compute node.

We defend against this type of attack in two ways:

• We supplement the built-in protections of Apple silicon with a hardened supply chain for PCC hardware, so that performing a hardware attack at scale would be both prohibitively expensive and likely to be discovered.
• We limit the impact of small-scale attacks by ensuring that they cannot be used to target the data of a specific user.

Private Cloud Compute hardware security starts at manufacturing, where we inventory and perform high-resolution imaging of the components of the PCC node before each server is sealed and its tamper switch is activated. When they arrive in the data center, we perform extensive revalidation before the servers are allowed to be provisioned for PCC. The process involves multiple Apple teams that cross-check data from independent sources, and the process is further monitored by a third-party observer not affiliated with Apple. At the end, a certificate is issued for keys rooted in the Secure Enclave UID for each PCC node. The user’s device will not send data to any PCC nodes if it cannot validate their certificates.

These processes broadly protect hardware from compromise. To guard against smaller, more sophisticated attacks that might otherwise avoid detection, Private Cloud Compute uses an approach we call target diffusion to ensure requests cannot be routed to specific nodes based on the user or their content.

Target diffusion starts with the request metadata, which leaves out any personally identifiable information about the source device or user, and includes only limited contextual data about the request that’s required to enable routing to the appropriate model. This metadata is the only part of the user’s request that is available to load balancers and other data center components running outside of the PCC trust boundary. The metadata also includes a single-use credential, based on RSA Blind Signatures , to authorize valid requests without tying them to a specific user. Additionally, PCC requests go through an OHTTP relay — operated by a third party — which hides the device’s source IP address before the request ever reaches the PCC infrastructure. This prevents an attacker from using an IP address to identify requests or associate them with an individual. It also means that an attacker would have to compromise both the third-party relay and our load balancer to steer traffic based on the source IP address.

User devices encrypt requests only for a subset of PCC nodes, rather than the PCC service as a whole. When asked by a user device, the load balancer returns a subset of PCC nodes that are most likely to be ready to process the user’s inference request — however, as the load balancer has no identifying information about the user or device for which it’s choosing nodes, it cannot bias the set for targeted users. By limiting the PCC nodes that can decrypt each request in this way, we ensure that if a single node were ever to be compromised, it would not be able to decrypt more than a small portion of incoming requests. Finally, the selection of PCC nodes by the load balancer is statistically auditable to protect against a highly sophisticated attack where the attacker compromises a PCC node as well as obtains complete control of the PCC load balancer.

Verifiable transparency

We consider allowing security researchers to verify the end-to-end security and privacy guarantees of Private Cloud Compute to be a critical requirement for ongoing public trust in the system. Traditional cloud services do not make their full production software images available to researchers — and even if they did, there’s no general mechanism to allow researchers to verify that those software images match what’s actually running in the production environment. (Some specialized mechanisms exist, such as Intel SGX and AWS Nitro attestation.)

When we launch Private Cloud Compute, we’ll take the extraordinary step of making software images of every production build of PCC publicly available for security research . This promise, too, is an enforceable guarantee: user devices will be willing to send data only to PCC nodes that can cryptographically attest to running publicly listed software. We want to ensure that security and privacy researchers can inspect Private Cloud Compute software, verify its functionality, and help identify issues — just like they can with Apple devices.

Our commitment to verifiable transparency includes:

• Publishing the measurements of all code running on PCC in an append-only and cryptographically tamper-proof transparency log.
• Making the log and associated binary software images publicly available for inspection and validation by privacy and security experts.
• Publishing and maintaining an official set of tools for researchers analyzing PCC node software.
• Rewarding important research findings through the Apple Security Bounty program.

Every production Private Cloud Compute software image will be published for independent binary inspection — including the OS, applications, and all relevant executables, which researchers can verify against the measurements in the transparency log. Software will be published within 90 days of inclusion in the log, or after relevant software updates are available, whichever is sooner. Once a release has been signed into the log, it cannot be removed without detection, much like the log-backed map data structure used by the Key Transparency mechanism for iMessage Contact Key Verification .

As we mentioned, user devices will ensure that they’re communicating only with PCC nodes running authorized and verifiable software images. Specifically, the user’s device will wrap its request payload key only to the public keys of those PCC nodes whose attested measurements match a software release in the public transparency log. And the same strict Code Signing technologies that prevent loading unauthorized software also ensure that all code on the PCC node is included in the attestation.

Making Private Cloud Compute software logged and inspectable in this way is a strong demonstration of our commitment to enable independent research on the platform. But we want to ensure researchers can rapidly get up to speed, verify our PCC privacy claims, and look for issues, so we’re going further with three specific steps:

• We’ll release a PCC Virtual Research Environment: a set of tools and images that simulate a PCC node on a Mac with Apple silicon, and that can boot a version of PCC software minimally modified for successful virtualization.
• While we’re publishing the binary images of every production PCC build, to further aid research we will periodically also publish a subset of the security-critical PCC source code.
• In a first for any Apple platform, PCC images will include the sepOS firmware and the iBoot bootloader in plaintext , making it easier than ever for researchers to study these critical components.

The Apple Security Bounty will reward research findings in the entire Private Cloud Compute software stack — with especially significant payouts for any issues that undermine our privacy claims.

More to come

Private Cloud Compute continues Apple’s profound commitment to user privacy. With sophisticated technologies to satisfy our requirements of stateless computation, enforceable guarantees, no privileged access, non-targetability, and verifiable transparency, we believe Private Cloud Compute is nothing short of the world-leading security architecture for cloud AI compute at scale.

We look forward to sharing many more technical details about PCC, including the implementation and behavior behind each of our core requirements. And we’re especially excited to soon invite security researchers for a first look at the Private Cloud Compute software and our PCC Virtual Research Environment.

Evaluating cat qubits for fault-tolerant quantum computing using Azure Quantum Resource Estimator

Mathias Soeken

Elie Gouzien

June 19th, 2024 0 1

Introduction

This blog post highlights a recent collaboration between Microsoft and Alice & Bob , a French startup whose goal is to build a fault-tolerant quantum computer by leveraging a superconducting qubit called a cat qubit . In this collaboration, Alice & Bob uses the new extensibility mechanisms of Microsoft’s Resource Estimator to obtain resource estimates for their cat qubit architecture.

The Resource Estimator is a tool that can help evaluate the practical benefit of quantum algorithms. It calculates an estimate for the expected runtime and the number of physical qubits needed to run a given program under different settings of the target fault-tolerant quantum computer. The default settings of the resource estimator represent generic gate-based and Majorana-based qubits, unbiased planar quantum error correction codes (i.e., 2D layout for logical qubits assuming the same error rates for bit flip and phase flip errors) that support lattice surgery, and T factories that use multiple rounds of distillation (please refer to this paper for more details on these assumptions). These settings cover many quantum computing architectures, but they do not have complete flexibility for quantum architects to model various other important system architectures with different assumptions.

Microsoft is happy to announce that the Resource Estimator, which was made open source in January 2024 , now has an extensibility API to model any quantum architecture and to modify all assumptions. To show how this extensibility API works, Microsoft and Alice & Bob demonstrate how it is used to model Alice & Bob’s cat qubit architecture, along with a biased repetition code, and Toffoli factories. The open-source example performs the resource estimation for elliptic curve cryptography described in Alice & Bob’s Physical Review Letters paper from July 2023.

Architecture

Cat qubits have special error correction requirements because they exhibit a biased noise: they have several orders of magnitude less bit flips than phase flips. They use engineered two photon dissipation to stabilize two coherent states of the same amplitude and opposite phase, used as the 0 and 1 of the qubits. The Alice & Bob roadmap takes advantage of this asymmetry to simplify the error correction strategy. To achieve this however, the usual hierarchy of gates used in quantum computing has to be modified. As a first step, we need to build a gate set that protects this noise-biasing property. And then, from this set, they have to offer a universal set of fault-tolerant operations (note that the bias-preserving gate set is typically not universal, but sufficient to implement a universal gate set at the logical level). This work is carried in the article Repetition Cat Qubits for Fault-Tolerant Quantum Computation and summarized in the figure below.

Alice & Bob’s architecture highlights the importance of extensibility in the Resource Estimator and the ability to override the pre-defined settings. The typical error correction code, used by the Resource Estimator, is the surface code, but cat qubits require a repetition code. The Resource Estimator assumes a “Clifford+T” universal gate set, while the gate set presented above for cat qubits is “Clifford+Toffoli.”

Implementation details

The resource estimator, which is written in Rust, can be extended by using a Rust API. The main function of the resource estimator is to calculate the physical resource estimates for a logical overhead with respect to an error correction protocol , a physical qubit , and a factory builder . The interaction of these components is illustrated in the architecture diagram above. Each of these components are interfaces that can be implemented, which allows full flexibility. For instance, the resource estimator doesn’t have to know about the input program, or even the layout method. It only needs the logical overhead, which gives the number of logical qubits, the logical depth, and the number of needed magic states. Likewise, the implementations of the other interfaces provide information for the resource estimation. We will explain some aspects of the implementation in the remainder of this section but please refer to the example source code in GitHub for more details.

The error correction protocol in the Resource Estimator defines both the physical qubit and the code parameter that it uses. For most codes, the code parameter is the code distance, and finding a value for the code distance that ensures a desired logical error rate given a physical qubit is one of the main goals of the error correction protocol. The Alice & Bob architecture uses a repetition code with two parameters: distance and the average number of photons. The distance deals with the phase flip error and the number of photons must be high enough to avoid bit flip errors, so that the repetition code can focus on correcting only the phase flip errors.

A factory builder’s job is to make magic state factories that produce magic states with a certain maximum output error probability. The factories can be either pre-computed or they can be calculated as needed, when a new request is made. Also, they can use the error correction protocol and select their own code parameters to make the factories. For Alice & Bob’s architecture, the magic state that is produced is CCX and there’s a pre-computed list of Toffoli factories available (see also Table 3 in the paper ).

We make two main assumptions about the input program: that it uses mostly CX (or CNOT) and CCX (or Toffoli) gates, and that they aren’t run in parallel, but each have their own cycle time (i.e., the number of needed error correction syndrome extraction cycles). With these assumptions, and the number of logical algorithm qubits before taking into account the layout, we can easily calculate the layout overhead as a function of the number of logical qubits and the number of CX and CCX gates. Moreover, the paper from Alice & Bob gives formulas to find values for these three metrics for the elliptic curve cryptography algorithm, and so the layout overhead can be generated as a function of the key size and some implementation details (such as the window size for windowed arithmetic). Moreover, we use the Azure Quantum Development Kit ( QDK ) to compute a logical overhead by evaluating a Q# program.

The above graph compares the space-time trade-off of resource estimates using the resource estimator and the estimates from the paper. The paper reported a quicker solution that needed more qubits, while the resource estimator produced estimates with fewer qubits and a longer, but feasible, runtime. Note that the resource estimator does not automatically explore application specific parameters (such as window sizes for windowed arithmetic).

You can try out and execute the Alice & Bob resource estimation example that uses Microsoft’s Resource Estimator . As it is open source, you can easily change the application input. The cost model that relies on CX and CCX gates is compatible with many logical resource estimation research papers in the literature, and therefore results from those papers can be quickly converted into physical resource estimates. Further, you can examine various Q# programs that are available in the Q# GitHub repository. We hope that the resource estimator gives you useful insights and helps your research; and we would welcome your feedback.

Mathias Soeken Senior Software Engineer, Quantum

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PQShield secures $37M more for ‘quantum resistant’ cryptography

Malicious hacking is getting increasingly sophisticated, and that’s leading to a very clear trend in security technology. To keep people and organizations safe, security also has to continue improving. 

Security startup PQShield has gotten an early start on that concept with a focus on “post-quantum” cryptography: Software and hardware solutions that, in theory, are future-proofed, capable of withstanding even hacks that will one day be carried out using the most powerful quantum machines. 

Now, to meet industry demand to build hardware and related systems based on its work, the company has raised a further $37 million in funding. 

Addition, Lee Fixel’s investment firm, led this Series B, with other strategic and financial backers participating, including Chevron Technology Ventures, Legal & General and Braavos Capital (all new investors), as well as existing investor Oxford Science Enterprises. Addition also led PQShield’s Series A in 2022 . The startup is not disclosing its valuation.

Dr. Ali El Kaafarani, a mathematician who founded the startup in Oxford, said the funding will be used to hire more talent and work more closely with its current and new customers and partners.

That list includes companies like AMD, Microchip Technologies, Collins Aerospace, Lattice Semiconductor, Sumitomo Electric, NTT Data and Mirise Technologies (Toyota / Denso R&D). The company also advises the White House, European Parliament, U.K. National Cyber Security Council and World Economic Forum. It also has worked with the biggest name in chips, Nvidia .

“We still have the highest density of cryptographers in the industry, in particular in the area of post-quantum cryptography,” he said in an interview. Added to that, there is an interesting development underway in terms of standardization that will also impact how the field evolves. 

The National Institute of Standards and Technology in the U.S. has been working for a decade on the idea of establishing post-quantum cryptographic standards. Those are now expected to be announced in the coming months, Dr. El Kaafarani said. “In just two or three weeks, we very much expect NIST to publish the official standards after publishing the drafts last August.”

One thing to watch is the role companies like PQShield — and others in the space like Xiphera , Post-Quantum and Palo Alto Networks — adopt as technology and computing continue to evolve. It’s also worth keeping an eye on how major companies adopt more sophisticated encryption to safeguard users’ data at both the software and hardware layers. 

Today, a lot of popular discourse around encryption has centered around how it can be used to safeguard messaging platforms. Notably, PQShield also provides its technology pro bono to the Signal Foundation, and is, per Dr. El Kaafarani, “working on different research projects with them.” In the realm of enterprise, it is working on how encryption is used in security systems to safeguard data both within company networks and when it is transported or shared outside them. 

The next stage of that discourse is likely to be around how data is handled in AI environments, both where AI is used and in the training of models. And of course, how to safeguard data in a world where malicious hackers are using AI to break through all protections.

Apple, as one example, is taking a new approach to privacy with a new approach it calls Private Cloud Compute , which it says enables “private AI processing” by integrating private clouds more tightly with its custom on-device silicon. 

“AI will be yet another reason why we need to make sure that our cryptography is up to date,” Dr. El Kaafarani said. “I believe that whether it’s Apple or others, you will see that they will start immediately using post-quantum cryptography for AI because they will not go through the legacy cryptography and then have to change to the new standards.”

PQShield offers solutions in three formats, which takes it out of the realm of straight deep tech and into a more commercializable tooling. It offers a system on a chip that is designed to sit on hardware like smartcards or processors; a cryptographic SDK that can be integrated into mobile and server apps to process data or run security operations; and a toolkit designed specifically to secure messaging services. That’s likely one reason why investors like Addition are interested, especially at a moment when computing and chips appear to be evolving so rapidly. 

“PQShield has continued its trajectory as a pioneer and leading authority in post-quantum cryptography for hardware and software. As we approach the culmination of the NIST PQC project, we expect newly-ratified standards to catalyze the quantum security market and drive rapid adoption of PQC across the technology supply chain,” Fixel said in a statement. “Thanks to an industry-leading team with decades of combined experience, PQShield has established a best-in-class product offering that is already leading the field. We are excited to see the company build on its existing commercial success and protect our digital future.”

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Microsoft Research Blog

Microsoft at cvpr 2024: innovations in computer vision and ai research.

Published June 17, 2024

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Microsoft is proud to sponsor the 41st annual Conference on Computer Vision and Pattern Recognition (CVPR 2024), held from June 17 to June 21. This premier conference covers a broad spectrum of topics in the field, including 3D reconstruction and modeling, action and motion analysis, video and image processing, synthetic data generation, neural networks, and many more. This year, 63 papers from Microsoft have been accepted, with six selected for oral presentations. This post highlights these contributions.

The diversity of these research projects reflects the interdisciplinary approach that Microsoft research teams have taken, from techniques that precisely recreate 3D human figures and perspectives in augmented reality (AR) to combining advanced image segmentation with synthetic data to better replicate real-world scenarios. Other projects demonstrate how researchers are combining machine learning with natural language processing and structured data, developing models that not only visualize but also interact with their environments. Collectively, these projects aim to improve machine perception and enable more accurate and responsive interactions with the world. 

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Oral presentations 

Bioclip: a vision foundation model for the tree of life.

Samuel Stevens, Jiaman Wu, Matthew J Thompson, Elizabeth G. Campolongo, Chan Hee Song, David Carlyn,  Li Dong , W. Dahdul, Charles Stewart, Tanya Y. Berger-Wolf, Wei-Lun Chao, Yu Su  

The surge in images captured from diverse sources—from drones to smartphones—offers a rich source of biological data. To harness this potential, we introduce TreeOfLife-10M, the largest and most diverse ML-ready dataset of biology images, and BioCLIP, a foundation model intended for the biological sciences. BioCLIP, utilizing the TreeOfLife-10M’s vast array of organism images and structured knowledge, excels in fine-grained biological classification, outperforming existing models by significant margins and demonstrating strong generalizability. 

EgoGen: An Egocentric Synthetic Data Generator

Gen Li, Kaifeng Zhao, Siwei Zhang, Xiaozhong Lyu, Mihai Dusmanu , Yan Zhang, Marc Pollefeys  

A critical challenge in augmented reality (AR) is simulating realistic anatomical movements to guide cameras for authentic egocentric views. To overcome this, the authors developed EgoGen, a sophisticated synthetic data generator that not only improves training data accuracy for egocentric tasks but also refines the integration of motion and perception. It offers a practical solution for creating realistic egocentric training data, with the goal of serving as a useful tool for egocentric computer vision research. 

Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks

Bin Xiao , Haiping Wu, Weijian Xu, Xiyang Dai , Houdong Hu, Yumao Lu, Michael Zeng , Ce Liu, Lu Yuan  

Florence-2 introduces a unified, prompt-based vision foundation model capable of handling a variety of tasks, from captioning to object detection and segmentation. Designed to interpret text prompts as task instructions, Florence-2 generates text outputs across a spectrum of vision and vision-language tasks. This model’s training utilizes the FLD-5B dataset, which includes 5.4 billion annotations on 126 million images, developed using an iterative strategy of automated image annotation and continual model refinement.

LISA: Reasoning Segmentation via Large Language Model

Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan , Shu Liu, Jiaya Jia

This work introduces reasoning segmentation , a new segmentation task using complex query texts to generate segmentation masks. The authors also established a new benchmark, comprising over a thousand image-instruction-mask data samples, incorporating intricate reasoning and world knowledge for evaluation. Finally, the authors present Large Language Instructed Segmentation Assistant (LISA), a tool that combines the linguistic capabilities of large language models with the ability to produce segmentation masks. LISA effectively handles complex queries and shows robust zero-shot learning abilities, further enhanced by minimal fine-tuning.

MultiPly: Reconstruction of Multiple People from Monocular Video in the Wild

Zeren Jiang, Chen Guo, Manuel Kaufmann, Tianjian Jiang, Julien Valentin (opens in new tab) , Otmar Hilliges, Jie Song  

MultiPly is a new framework for reconstructing multiple people in 3D from single-camera videos in natural settings. This technique employs a layered neural representation for the entire scene, refined through layer-wise differentiable volume rendering. Enhanced by a hybrid instance segmentation that combines self-supervised 3D and promptable 2D techniques, it provides reliable segmentation even with close interactions. The process uses confidence-guided optimization to alternately refine human poses and shapes, achieving high-fidelity, consistent 3D models.

SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes

Alexandros Delitzas, Ayça Takmaz, Federico Tombari, Robert Sumner, Marc Pollefeys , Francis Engelmann  

Traditional 3D scene understanding methods are heavily focused on 3D sematic and instance segmentation, but the true challenge lies in interacting with functional interactive elements like handles, knobs, and buttons to achieve specific tasks. Enter SceneFun3D: a robust dataset featuring over 14,800 precise interaction annotations across 710 high-resolution real-world 3D indoor scenes. This dataset enriches scene comprehension with motion parameters and task-specific natural language descriptions, facilitating advanced research in functionality segmentation, task-driven affordance grounding, and 3D motion estimation.

Discover more about our work and contributions to CVPR 2024, including our full list of publications and sessions , on our conference webpage . 

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Advanced analysis and validation of a microrna signature for fanconi anemia.

1. Introduction

2. results and discussion, 3. conclusions, 4. materials and methods, 4.1. bioinformatic approach, 4.2. bm samples, 4.3. rna isolation, expression of mirna, and selected gene targets, 4.4. statistical analysis, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Degan, P.; Cappelli, E.; Longobardi, M.; Pulliero, A.; Cuccarolo, P.; Dufour, C.; Ravera, S.; Calzia, D.; Izzotti, A. A Global MicroRNA Profile in Fanconi Anemia: A Pilot Study. Metab. Syndr. Relat. Disord. 2019 , 17 , 53–59. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Iwakawa, H.O.; Tomari, Y. The Functions of MicroRNAs: mRNA Decay and Translational Repression. Trends Cell. Biol. 2015 , 11 , 651–665. [ Google Scholar ] [ CrossRef ]
• Lewis, B.P.; Burge, C.B.; Bartel, D.P. Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets. Cell 2005 , 1 , 15–20. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Peng, Y.; Croce, C.M. The role of MicroRNAs in human cancer. Signal Transduct. Target. Ther. 2016 , 1 , 15004. [ Google Scholar ] [ CrossRef ]
• Çorapçıoğlu, M.; Oğul, H. miSEA: microRNA set enrichment analysis. Biosystems 2015 , 134 , 37–42. [ Google Scholar ] [ CrossRef ]
• Griffiths-Jones, S.; Grocock, R.J.; Van Dongen, S.; Bateman, A.; Enright, A.J. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006 , 34 , D140–D144. [ Google Scholar ] [ CrossRef ]
• Hsu, J.B.-K.; Chiu, C.-M.; Hsu, S.-D.; Huang, W.-Y.; Chien, C.-H.; Lee, T.-Y.; Huang, H.-D. miRTar: An integrated system for identifying miRNA-target interactions in human. BMC Bioinform. 2011 , 12 , 300. [ Google Scholar ] [ CrossRef ]
• Godard, P.; van Eyll, J. Pathway analysis from lists of microRNAs: Common pitfalls and alternative strategy. Nucleic Acids Res. 2015 , 43 , 3490–3497. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Calin, G.A.; Croce, C.M. MicroRNA Signatures in Human Cancers. Nat. Rev. Cancer 2006 , 11 , 857–866. [ Google Scholar ] [ CrossRef ]
• Fu, R.; Meng, K.; Zhang, R.; Du, X.; Jiao, J. Bone marrow-derived exosomes promote inflammation and osteoclast differentiation in high-turnover renal osteodystrophy. Ren. Fail. 2023 , 2 , 2264396. [ Google Scholar ] [ CrossRef ]
• Fry, C.S.; Kirby, T.J.; Kosmac, K.; McCarthy, J.J.; Peterson, C.A. Myogenic Progenitor Cells Control Extracellular Matrix Production by Fibroblasts during Skeletal Muscle Hypertrophy. Cell Stem Cell 2017 , 1 , 56–69. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Feng, C.; Jin, X.; Han, Y.; Guo, R.; Zou, J.; Li, Y.; Wang, Y. Expression and Prognostic Analyses of ITGA3, ITGA5, and ITGA6 in Head and Neck Squamous Cell Carcinoma. Med Sci. Monit. 2020 , 26 , e926800. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Méndez-López, I.; Worman, H.J. Inner nuclear membrane proteins: Impact on human disease. Chromosoma 2012 , 2 , 153–167. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Peng, Y.; Wu, D.; Li, F.; Zhang, P.; Feng, Y.; He, A. Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis. Cancer Cell Int. 2020 , 20 , 262. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Scott, L.E.; Weinberg, S.H.; Lemmon, C.A. Mechanochemical Signaling of the Extracellular Matrix in Epithelial-Mesenchymal Transition. Front. Cell Dev. Biol. 2019 , 7 , 135. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Becker-Weimann, S.; Xiong, G.; Furuta, S.; Han, J.; Kuhn, I.; Akavia, U.-D.; Pe’er, D.; Bissell, M.J.; Xu, R. NFkB disrupts tissue polarity in 3D by preventing integration of microenvironmental signals. Oncotarget 2013 , 11 , 2010–2020. [ Google Scholar ] [ CrossRef ]
• Beaman, E.-M.; Carter, D.R.F.; A Brooks, S. GALNTs: Master regulators of metastasis-associated epithelial-mesenchymal transition (EMT)? Glycobiology 2022 , 7 , 556–579. [ Google Scholar ] [ CrossRef ]
• Ravera, S.; Capanni, C.; Tognotti, D.; Bottega, R.; Columbaro, M.; Dufour, C.; Cappelli, E.; Degan, P. Inhibition of Metalloproteinase Activity in FANCA Is Linked to Altered Oxygen Metabolism. J. Cell. Physiol. 2015 , 3 , 603–609. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shyamsunder, P.; Verma, R.S.; Lyakhovich, A. ROMO1 regulates RedOx states and serves as an inducer of NF-κB-driven EMT factors in Fanconi anemia. Cancer Lett. 2015 , 1 , 33–38. [ Google Scholar ] [ CrossRef ]
• Epanchintsev, A.; Shyamsunder, P.; Verma, R.S.; Lyakhovich, A. IL-6, IL-8, MMP-2, MMP-9 are overexpressed in Fanconi anemia cells through a NF-κB/TNF-α dependent mechanism. Mol. Carcinog. 2015 , 12 , 1686–1699. [ Google Scholar ] [ CrossRef ]
• Ghafouri-Fard, S.; Khoshbakht, T.; Hussen, B.M.; Taheri, M.; Samadian, M. A Review on the Role of miR-1246 in the Pathoetiology of Different Cancers. Front. Mol. Biosci. 2022 , 8 , 771835. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Xu, R.; Li, H.; Wu, S.; Qu, J.; Yuan, H.; Zhou, Y.; Lu, Q. MicroRNA-1246 regulates the radio-sensitizing effect of curcumin in bladder cancer cells via activating P53. Int. Urol. Nephrol. 2019 , 10 , 1771–1779. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Liao, J.-M.; Zhou, X.; Zhang, Y.; Lu, H. MiR-1246: A new link of the p53 family with cancer and Down syndrome. Cell Cycle 2012 , 14 , 2624–2630. [ Google Scholar ] [ CrossRef ]
• Zhang, Q.; Liu, S.; Zhang, J.; Ma, X.; Dong, M.; Sun, B.; Xin, Y. Roles and regulatory mechanisms of miR-30b in cancer, cardiovascular disease, and metabolic disorders (Review). Exp. Ther. Med. 2021 , 1 , 44. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Walter, D.; Lier, A.; Geiselhart, A.; Thalheimer, F.B.; Huntscha, S.; Sobotta, M.C.; Moehrle, B.; Brocks, D.; Bayindir, I.; Kaschutnig, P.; et al. Exit from dormancy provokes DNA-damage-induced attrition in haematopoietic stem cells. Nature 2015 , 7548 , 549–552. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Marion, W.; Boettcher, S.; Ruiz-Torres, S.; Lummertzda Rocha, E.; Lundin, V.; Morris, V.; Chou, S.; Zhao, A.M.; Kubaczka, C.; Aumais, O.; et al. An induced pluripotent stem cell model of Fanconi anemia reveals mechanisms of p53-driven progenitor cell differentiation. Blood Adv. 2020 , 19 , 4679–4692. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, X.; Sun, Q. TP53 mutations, expression and interaction networks in human cancers. Oncotarget 2017 , 8 , 624–643. [ Google Scholar ] [ CrossRef ]
• Cappelli, E.; Bertola, N.; Bruno, S.; Degan, P.; Regis, S.; Corsolini, F.; Banelli, B.; Dufour, C.; Ravera, S. A Multidrug Approach to Modulate the Mitochondrial Metabolism Impairment and Relative Oxidative Stress in Fanconi Anemia Complementation Group A. Metabolites 2021 , 12 , 6. [ Google Scholar ] [ CrossRef ]
• Huang, Y.; Xie, B.; Cao, M.; Lu, H.; Wu, X.; Hao, Q.; Zhou, X. LncRNA RNA Component of Mitochondrial RNA-Processing Endoribonuclease Promotes AKT-Dependent Breast Cancer Growth and Migration by Trapping MicroRNA-206. Front. Cell Dev. Biol. 2021 , 9 , 730538. [ Google Scholar ] [ CrossRef ]
• Zhang, C.; Cao, W.; Wang, J.; Liu, J.; Liu, J.; Wu, H.; Li, S.; Zhang, C. A prognostic long non-coding RNA-associated competing endogenous RNA network in head and neck squamous cell carcinoma. Peer J 2020 , 8 , e9701. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Liao, J.; Zeng, S.X.; Lu, H. p53 downregulates Down syndrome-associated DYRK1A through miR-1246. EMBO Rep. 2011 , 12 , 811–817. [ Google Scholar ] [ CrossRef ]
• Vaxevanis, C.K.; Bauer, M.; Subbarayan, K.; Friedrich, M.; Massa, C.; Biehl, K.; Al-Ali, H.K.; Wickenhauser, C.; Seliger, B. Biglycan as a mediator of proinflammatory response and target for MDS and sAML therapy. Oncoimmunology 2022 , 12 , 2152998. [ Google Scholar ] [ CrossRef ]
• Kozlov, E.; Shidlovskii, Y.V.; Gilmutdinov, R.; Schedl, P.; Zhukova, M. The role of CPEB family proteins in the nervous system function in the norm and pathology. Cell Biosci. 2021 , 11 , 64. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fagundes, R.; Teixeira, L.K. Cyclin E/CDK2: DNA Replication, Replication Stress and Genomic Instability. Front. Cell Dev. Biol. 2021 , 9 , 774845. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kciuk, M.; Gielecińska, A.; Mujwar, S.; Mojzych, M.; Kontek, R. Cyclin-dependent kinases in DNA damage response. Biochim. Biophys. Acta Rev. Cancer 2022 , 1877 , 188716. [ Google Scholar ] [ CrossRef ]
• Nasa, I.; Cressey, L.E.; Kruse, T.; Hertz, E.P.T.; Gui, J.; Graves, L.M.; Nilsson, J.; Kettenbach, A.N. Quantitative kinase and phosphatase profiling reveal that CDK1 phosphorylates PP2Ac to promote mitotic entry. Sci. Signal. 2020 , 13 , eaba7823. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kirova, D.G.; Judasova, K.; Vorhauser, J.; Zerjatke, T.; Leung, J.K.; Glauche, I.; Mansfeld, J. A ROS-dependent mechanism promotes CDK2 phosphorylation to drive progression through S phase. Dev. Cell 2022 , 57 , 1712–1727.e9. [ Google Scholar ] [ CrossRef ]
• Chen, P.-J.; Weng, J.-Y.; Hsu, P.-H.; Shew, J.-Y.; Huang, Y.-S.; Lee, W.-H. NPGPx modulates CPEB2-controlled HIF-1α RNA translation in response to oxidative stress. Nucleic Acids Res. 2015 , 43 , 9393–9404. [ Google Scholar ] [ CrossRef ]
• Elgenaidi, I.; Spiers, J. Regulation of the phosphoprotein phosphatase 2A system and its modulation during oxidative stress: A potential therapeutic target? Pharmacol. Ther. 2019 , 198 , 68–89. [ Google Scholar ] [ CrossRef ]
• Ravera, S.; Degan, P.; Sabatini, F.; Columbaro, M.; Dufour, C.; Cappelli, E. Altered lipid metabolism could drive the bone marrow failure in fanconi anaemia. Br. J. Haematol. 2019 , 184 , 693–696. [ Google Scholar ] [ CrossRef ]
• Cappelli, E.; Degan, P.; Bruno, S.; Pierri, F.; Miano, M.; Raggi, F.; Farruggia, P.; Mecucci, C.; Crescenzi, B.; Naim, V.; et al. The passage from bone marrow niche to bloodstream triggers the metabolic impairment in Fanconi Anemia mononuclear cells. Redox Biol. 2020 , 36 , 101618. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cappelli, E.; Degan, P.; Dufour, C.; Ravera, S. Aerobic metabolism dysfunction as one of the links between Fanconi anemia-deficient pathway and the aggressive cell invasion in head and neck cancer cells. Oral Oncol. 2018 , 87 , 210–211. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Liu, W.; Li, H.; Wang, Y.; Zhao, X.; Guo, Y.; Jin, J.; Chi, R. MiR-30b-5p functions as a tumor suppressor in cell proliferation, metastasis and epithelial-to-mesenchymal transition by targeting G-protein subunit α-13 in renal cell carcinoma. Gene 2017 , 626 , 275–281. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Qin, X.; Chen, J.; Wu, L.; Liu, Z. MiR-30b-5p acts as a tumor suppressor, repressing cell proliferation and cell cycle in human hepatocellular carcinoma. Biomed. Pharmacother. 2017 , 89 , 742–750. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wu, T.; Tian, Q.; Liu, R.; Xu, K.; Shi, S.; Zhang, X.; Gao, L.; Yin, X.; Xu, S.; Wang, P. Inhibitory role of bone marrow mesenchymal stem cells-derived exosome in non-small-cell lung cancer: microRNA-30b-5p, EZH2 and PI3K/AKT pathway. J. Cell. Mol. Med. 2023 , 27 , 3526–3538. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Qiu, H.; Shen, X.; Chen, B.; Chen, T.; Feng, G.; Chen, S.; Feng, D.; Xu, Q. miR-30b-5p inhibits cancer progression and enhances cisplatin sensitivity in lung cancer through targeting LRP8. Apoptosis 2021 , 26 , 261–276. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Huang, D.; Qu, D. Early diagnostic and prognostic value of serum exosomal miR-1246 in non-small cell lung cancer. Int. J. Clin. Exp. Pathol. 2020 , 13 , 1601–1607. [ Google Scholar ] [ PubMed ]
• Purvis, N.; Kumari, S.; Chandrasekera, D.; Papannarao, J.B.; Gandhi, S.; van Hout, I.; Coffey, S.; Bunton, R.; Sugunesegran, R.; Parry, D.; et al. Diabetes induces dysregulation of microRNAs associated with survival, proliferation and self-renewal in cardiac progenitor cells. Diabetologia 2021 , 64 , 1422–1435. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, J.; Sipple, J.; Maynard, S.; Mehta, P.A.; Rose, S.R.; Davies, S.M.; Pang, Q. Fanconi Anemia Links Reactive Oxygen Species to Insulin Resistance and Obesity. Antioxid. Redox Signal. 2012 , 17 , 1083–1098. [ Google Scholar ] [ CrossRef ]
• Li, J.; Li, X.; Sun, W.; Zhang, J.; Yan, Q.; Wu, J.; Jin, J.; Lu, R.; Miao, D. Specific overexpression of SIRT1 in mesenchymal stem cells rescues hematopoiesis niche in BMI1 knockout mice through promoting CXCL12 expression. Int. J. Biol. Sci. 2022 , 18 , 2091–2103. [ Google Scholar ] [ CrossRef ]
• Pagano, G.; Pallardó, F.V.; Porto, B.; Fittipaldi, M.R.; Lyakhovich, A.; Trifuoggi, M. Mitoprotective Clinical Strategies in Type 2 Diabetes and Fanconi Anemia Patients: Suggestions for Clinical Management of Mitochondrial Dysfunction. Antioxidants 2020 , 9 , 82. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pang, X.; Li, H.; Guan, F.; Li, X. Multiple Roles of Glycans in Hematological Malignancies. Front. Oncol. 2018 , 8 , 364. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mao, C.; Zhuang, S.-M.; Xia, Z.; Xiao, Z.-W.; Huang, C.-X.; Su, Q.; Chen, J.; Liao, J. Pan-cancer analysis of GALNTs expression identifies a prognostic of GALNTs feature in low grade glioma. J. Leukoc. Biol. 2022 , 112 , 887–899. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Freire, T.; Berois, N.; Sóñora, C.; Varangot, M.; Barrios, E.; Osinaga, E. UDP- N -acetyl-D-galactosamine:polypeptide N -acetylgalactosaminyltransferase 6 (ppGalNAc-T6) mRNA as a potential new marker for detection of bone marrow-disseminated breast cancer cells. Int. J. Cancer. 2006 , 119 , 1383–1388. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Inose, H.; Ochi, H.; Kimura, A.; Fujita, K.; Xu, R.; Sato, S.; Iwasaki, M.; Sunamura, S.; Takeuchi, Y.; Fukumoto, S.; et al. A microRNA regulatory mechanism of osteoblast differentiation. Proc. Natl. Acad. Sci. USA 2009 , 106 , 20794–20799. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, Y.; Yang, Y.-R.; Fan, X.-L.; Lin, P.; Yang, H.; Chen, X.-Z.; Xu, X.-D. miR-206 inhibits osteogenic differentiation of bone marrow mesenchymal stem cells by targetting glutaminase. Biosci. Rep. 2019 , 39 , BSR20181108. [ Google Scholar ] [ CrossRef ]
• Wang, L.; Zhang, H.; Wang, S.; Chen, X.; Su, J. Bone Marrow Adipocytes: A Critical Player in the Bone Marrow Microenvironment. Front. Cell Dev. Biol. 2021 , 9 , 770705. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Flamant, S.; Chomel, J.-C.; Desterke, C.; Féraud, O.; Gobbo, E.; Mitjavila-Garcia, M.-T.; Foudi, A.; Griscelli, F.; Turhan, A.G.; Bennaceur-Griscelli, A. Global MicroRNA Profiling Uncovers miR-206 as a Negative Regulator of Hematopoietic Commitment in Human Pluripotent Stem Cells. Int. J. Mol. Sci. 2019 , 20 , 1737. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Lei, W.; Yan, W.; Li, X.; Wang, X.; Zhao, Z.; Hui, J.; Shen, Z.; Yang, J. microRNA-206 is involved in survival of hypoxia preconditioned mesenchymal stem cells through targeting Pim-1 kinase. Stem Cell Res. Ther. 2016 , 7 , 61. [ Google Scholar ] [ CrossRef ]
• Liu, X.; Yang, Z.; Meng, Q.; Chen, Y.; Shao, L.; Li, J.; Chen, Y.; Shen, Z. Downregulation of MicroRNA-206 Alleviates the Sublethal Oxidative Stress-Induced Premature Senescence and Dysfunction in Mesenchymal Stem Cells via Targeting Alpl. Oxid. Med. Cell. Longev. 2020 , 2020 , 7242836. [ Google Scholar ] [ CrossRef ]
• Samaeekia, R.; Adorno-Cruz, V.; Bockhorn, J.; Chang, Y.-F.; Huang, S.; Prat, A.; Ha, N.; Kibria, G.; Huo, D.; Zheng, H.; et al. miR-206 Inhibits Stemness and Metastasis of Breast Cancer by Targeting MKL1/IL11 Pathway. Clin. Cancer Res. 2017 , 23 , 1091–1103. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, Q.; Gu, Z.; Tan, Q.; Ren, L.; Chen, S. MicroRNA-129-1-3p Represses the Progression of Triple-Negative Breast Cancer by Targeting the GRIN2D Gene. Biomed. Res. Int. 2022 , 2022 , 1549357. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vlachos, I.S.; Kostoulas, N.; Vergoulis, T.; Georgakilas, G.; Reczko, M.; Maragkakis, M.; Paraskevopoulou, M.D.; Prionidis, K.; Dalamagas, T.; Hatzigeorgiou, A.G. DIANA miRPath v.2.0: Investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res. 2012 , 40 , W498–W504. [ Google Scholar ] [ CrossRef ]
• Licursi, V.; Conte, F.; Fiscon, G.; Paci, P. MIENTURNET: An interactive web tool for microRNA-target enrichment and network-based analysis. BMC Bioinform. 2019 , 1 , 545. [ Google Scholar ] [ CrossRef ] [ PubMed ]

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Cappelli, E.; Ravera, S.; Bertola, N.; Grilli, F.; Squillario, M.; Regis, S.; Degan, P. Advanced Analysis and Validation of a microRNA Signature for Fanconi Anemia. Genes 2024 , 15 , 820. https://doi.org/10.3390/genes15070820

Cappelli E, Ravera S, Bertola N, Grilli F, Squillario M, Regis S, Degan P. Advanced Analysis and Validation of a microRNA Signature for Fanconi Anemia. Genes . 2024; 15(7):820. https://doi.org/10.3390/genes15070820

Cappelli, Enrico, Silvia Ravera, Nadia Bertola, Federica Grilli, Margherita Squillario, Stefano Regis, and Paolo Degan. 2024. "Advanced Analysis and Validation of a microRNA Signature for Fanconi Anemia" Genes 15, no. 7: 820. https://doi.org/10.3390/genes15070820

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