International Association
for Cryptologic Research

The Cryptography Research Group at University College Cork (UCC) is looking for a highly motivated Post-Doctoral or Senior Post-Doctoral Researcher in homomorphic encryption. The researcher will be employed on the Horizon Europe project “SECURED”, aimed at scaling up the secure processing of health data, and will focus on homomorphic encryption and secure multi-party computation, and how they can be efficiently used in the e-health context. The Principal Investigator of the project in UCC is Dr. Paolo Palmieri.

The candidate should hold a PhD degree in cryptography or related area, with a good track record of publications. Ideally, they will have experience in homomorphic encryption, or related areas such as lattice-based or other post-quantum cryptography, secure multiparty computation etc. Candidates with a background in other areas of cryptography, but with a strong interest in homomorphic encryption will also be considered. A strong mathematical background is expected, complemented with programming skills. Experience with relevant libraries such as SEAL, HElib etc. is an asset.

The position is for 2 years, with a possibility of extension subject to availability of funding. The successful candidate will be appointed at Post-Doctoral or Senior Post-Doctoral level depending on their experience and qualifications. A budget for travel, equipment, publications and other research expenses is available as part of the project.

The Cryptography Research Group is led by Dr. Paolo Palmieri and consists of 8 researchers at doctoral and post-doctoral level. The hired researcher will be encouraged to collaborate with other members of the group, and to take a mentoring role with some of the more junior researchers. There will also be ample opportunities to work with other partners in the SECURED project (including some of the top research groups in cryptography, both in industry and academia), as well as with the group’s extensive network of international collaborations.

Closing date for applications:

Contact: Informal inquiries can be made by e-mail to Paolo Palmieri at p.palmieri@cs.ucc.ie but applications must be made online at http://ore.ucc.ie/ (reference number 069532) before 12:00 (noon), August 10, 2023.

More information: https://security.ucc.ie/vacancies.html

We are seeking a PhD candidate interested in interdisciplinary research on the development, efficient implementation (hardware and software), use, orchestration, and improvement of privacy-preserving and data anonymization techniques.


What are you going to do?

Carry out original research in the field of implementation and applications of privacy preserving technologies for data analytics in healthcare

Be active in the fundamental and/or applied research area, publishing in high level international journals and presenting at leading conferences

Take part in ongoing educational activities, such as assisting in a course and guiding student thesis projects, at the BSc or MSc level

Collaborate with other groups, institutes and/or companies by contributing expertise to joint research projects

Contribute to activities and deliverables of the SECURED Horizon Europe Project


What do you have to offer?

An MSc degree in Computer Science, Computer Engineering, or Electrical Engineering (or a related discipline)

Strong analytical and technical skills, good problem-solving skills

An interdisciplinary mindset and an open and proactive personality in interacting with researchers from different disciplines

A strong scientific interest in security and privacy, in particular in at least one of the following fields: efficient implementation of cryptographic and privacy preserving primitives, both in hardware and in software application, orchestration, and improvement of privacy-preserving techniques to achieve given data protection objectives

The willingness to work in a highly international research team

Fluency in oral and written English and good presentation skills

Closing date for applications:

Contact: Francesco Regazzoni

More information: https://vacatures.uva.nl/UvA/job/PhD-Position-on-Efficient-Privacy-preserving-Techniques-for-Data-Analysis-and-Machine-Learning/760571702/

As an experiment to manage the problem of growing program committee sizes, the PC chairs of Crypto 2024 are soliciting nominations (including self-nominations) for program committee service. The bulk of the work will take place from mid-February to the first week of May. Each PC member will be expected to review approximately 15 papers.

Please submit nominations via this form by July 31: https://forms.gle/wwvx4SkAoooX5SEA9

As a multi-receiver variants of public key encryption with keyword search (PEKS), broadcast encryption with keyword search (BEKS) has been proposed (Attrapadung et al. at ASIACRYPT 2006/Chatterjee-Mukherjee at INDOCRYPT 2018). Unlike broadcast encryption, no receiver anonymity is considered because the test algorithm takes a set of receivers as input and thus a set of receivers needs to be contained in a ciphertext. In this paper, we propose a generic construction of BEKS from anonymous and weakly robust 3-level hierarchical identity-based encryption (HIBE). The proposed generic construction provides outsider anonymity, where an adversary is allowed to obtain secret keys of outsiders who do not belong to the challenge sets, and provides sublinear-size ciphertext in terms of the number of receivers. Moreover, the proposed construction considers security against chosen-ciphertext attack (CCA) where an adversary is allowed to access a test oracle in the searchable encryption context. The proposed generic construction can be seen as an extension to the Fazio-Perera generic construction of anonymous broadcast encryption (PKC 2012) from anonymous and weakly robust identity-based encryption (IBE) and the Boneh et al. generic construction of PEKS (EUROCRYPT 2004) from anonymous IBE. We run the Fazio-Perera construction employs on the first-level identity and run the Boneh et al. generic construction on the second-level identity, i.e., a keyword is regarded as a second-level identity. The third-level identity is used for providing CCA security by employing one-time signatures. We also introduce weak robustness in the HIBE setting, and demonstrate that the Abdalla et al. generic transformation (TCC 2010/JoC 2018) for providing weak robustness to IBE works for HIBE with an appropriate parameter setting. We also explicitly introduce attractive concrete instantiations of the proposed generic construction from pairings and lattices, respectively.

Jitsi Meet is an open-source video conferencing system, and a popular alternative to proprietary services such as Zoom and Google Meet. The Jitsi project makes strong privacy and security claims in its advertising, but there is no published research into the merits of these claims. Moreover, Jitsi announced end-to-end encryption (E2EE) support in April 2020, and prominently features this in its marketing.

We present an in-depth analysis of the design of Jitsi and its use of cryptography. Based on our analysis, we demonstrate two practical attacks that compromised server components can mount against the E2EE layer: we show how the bridge can break integrity by injecting inauthentic media into E2EE conferences, whilst the signaling server can defeat the encryption entirely. On top of its susceptibility to these attacks, the E2EE feature does not apply to text-based communications. This is not made apparent to users and would be a reasonable expectation given how Jitsi is marketed. Further, we identify critical issues with Jitsi's poll feature, which allow any meeting participant to arbitrarily manipulate voting results. Our findings are backed by proof-of-concept implementations and were verified to be exploitable in practice.

We communicated our findings to Jitsi via a coordinated disclosure process. Jitsi has addressed the vulnerabilities via a mix of technical improvements and documentation changes.

Masking is a well-studied method for achieving provable security against side-channel attacks. In masking, each sensitive variable is split into $d$ randomized shares, and computations are performed with those shares. In addition to the computational overhead of masked arithmetic, masking also has a storage cost, increasing the requirements for working memory and secret key storage proportionally with $d$.

In this work, we introduce mask compression. This conceptually simple technique is based on standard, non-masked symmetric cryptography. Mask compression allows an implementation to dynamically replace individual shares of large arithmetic objects (such as polynomial rings) with $\kappa$-bit cryptographic seeds (or temporary keys) when they are not in computational use. Since $\kappa$ does not need to be larger than the security parameter (e.g., $\kappa=256$ bits) and each polynomial share may be several kilobytes in size, this radically reduces the memory requirement of high-order masking. Overall provable security properties can be maintained by using appropriate gadgets to manage the compressed shares. We describe gadgets with Non-Inteference (NI) and composable Strong-Non Interference (SNI) security arguments.

Mask compression can be applied in various settings, including symmetric cryptography, code-based cryptography, and lattice-based cryptography. It is especially useful for cryptographic primitives that allow quasilinear-complexity masking and hence are practically capable of very high masking orders. We illustrate this with a $d=32$ (Order-31) implementation of the recently introduced lattice-based signature scheme Raccoon on an FPGA platform with limited memory resources.

This paper explores the use of a system of equations to factor semiprime numbers. Semiprime numbers are a special type of omposite number that are the product of two prime numbers. Factoring semiprime numbers is important in cryptography and number theory. In this study, we present a method that applies a system of polynomial equations to factor semiprime number $M$. Where $M$ can be any semiprime number. In fact, we build a family of systems where each system compose from three polynomial equations with three variables. The results of this study show that a solution for one system results with a complete factorization for a semiprime number. It may be possible to apply well known algorithms, such as Grobner method to solve one of those systems for a particular semiprime number $M$.

We optimize Zero Knowledge (ZK) proofs of statements expressed as RAM programs over arithmetic values. Our arithmetic-circuit-based read/write memory uses only 4 input gates and 6 multiplication gates per memory access. This is an almost 3× total gate improvement over prior state of the art (Delpech de Saint Guilhem et al., SCN’22).

We implemented our memory in the context of ZK proofs based on vector oblivious linear evaluation (VOLE), and we further optimize based on techniques available in the VOLE setting. Our experiments show that (1) our total runtime improves over that of the prior best VOLE-ZK RAM (Franzese et al., CCS’21) by up to 20× and (2) on a typical hardware setup, we can achieve ≈ 600K RAM accesses per second.

We also develop improved read-only memory and set ZK data structures. These are used internally in our read/write memory and improve over prior work.

Grover’s algorithm is a very versatile cryptanalytical tool. Even though it doesn’t provide an exponential speed-up, it still changed the cryptographic requirements all over the world. Usually, Grover’s algorithm is executed with a fixed well-defined function indicating good states. In this paper, we want to investigate what happens if the function is changed over time to mark less and less good states. We compute the amplitudes after $2^{s/2}$ steps of an adjusted Grover’s algorithm proposed by Zheng et al. in Nested Quantum Search Model on Symmetric Ciphers and Its Applications (2023). We use the amplitudes to reason that such an approach always leads to a worse run-time when compared to the naïve version. We also indicate at which point in Zheng et al. the counterintuitive nature of quantum computation leads to false assumptions.

In 2018, Eric Bond Hutton posed a challenge online involving a classical cipher of his creation. It was broken nearly two years later by brute-forcing the keywords, and a new challenge that involves a modified cipher was posted. This is an explanation of how we broke the second challenge. We did so by scanning all books on Project Gutenberg for an acceptable match, then resolving any discrepancies and finding the keys.

Decentralized apps (DApps) often hold significant cryptocurrency assets. In order to manage these assets and coordinate joint investments, shareholders leverage the underlying smart contract functionality to realize a transparent, verifiable, and secure decision-making process. That is, DApps implement proposal-based voting. Permissionless blockchains, however, lead to a conflict between transparency and anonymity; potentially preventing free decision-making if individual votes and intermediate results become public. In this paper, we therefore present Tornado Vote, a voting DApp for anonymous, fair, and practical voting on the Ethereum blockchain. We propose to use a cryptocurrency mixer such as Tornado Cash to reconcile transparency and anonymity. To this end, we adapt Tornado Cash and develop a voting protocol that implements a fair voting process. While Tornado Vote can technically process 10k votes on Ethereum in approximately two hours, this is not feasible under realistic conditions: Third-party transactions on the Ethereum Mainnet reduce the possible throughput, and transaction fees make it infeasible to use all available block capacities. We therefore present various Gas cost models that yield lower bounds and economic estimations with respect to the required number of blocks and voting costs to assess and adjust Tornado Vote's feasibility trade-off.

Fully homomorphic encryption suffers from a large expansion in the size of encrypted data, which makes FHE impractical for low-bandwidth networks. Fortunately, transciphering allows to circumvent this issue by involving a symmetric cryptosystem which does not carry the disadvantage of a large expansion factor, and maintains the ability to recover an FHE ciphertext with the cost of extra homomorphic computations on the receiver side. Recent works have started to investigate the efficiency of TFHE as the FHE layer in transciphering, combined with various symmetric schemes including a NIST finalist for lightweight cryptography, namely Grain128-AEAD. Yet, this has so far been done without taking advantage of TFHE functional bootstrapping abilities, that is, evaluating any discrete function ``for free'' within the bootstrapping operation. In this work, we thus investigate the use of TFHE functional bootstrapping for implementing Grain128-AEAD in a more efficient base ($B > 2$) representation, rather than a binary one. This significantly reduces the overall number of necessary bootstrappings in a homomorphic run of the stream-cipher, for example reducing the number of bootstrappings required in the warm-up phase by a factor of $\approx$ 3 when $B=16$.

Event date: 23 October to 26 October 2023
Submission deadline: 15 June 2023
Notification: 28 July 2023

Event date: 24 June to 26 June 2024

Profiling side-channel analysis has gained widespread acceptance in both academic and industrial realms due to its robust capacity to unveil protected secrets, even in the presence of countermeasures. To harness this capability, an adversary must access a clone of the target device to acquire profiling measurements, labeling them with leakage models. The challenge of finding an effective leakage model, especially for a protected dataset with a low signal-to-noise ratio or weak correlation between actual leakages and labels, often necessitates an intuitive engineering approach, as otherwise, the attack will not perform well.

In this paper, we introduce a deep learning approach that does not assume any specific leakage model, referred to as the multibit model. Instead of trying to learn a representation of the target intermediate data (label), we utilize the concept of the stochastic model to decompose the label into bits. Then, the deep learning model is used to classify each bit independently. This versatile multibit model can align with existing leakage models like the Hamming weight and Most Significant Bit leakage models while also possessing the flexibility to adapt to complex leakage scenarios. To further improve the attack efficiency, we extend the multibit model to simultaneously attack all 16 subkey bytes, which requires negligible computational effort. Based on our preliminary analysis, two of the four considered datasets could only be broken using a Hamming Weight leakage model. Using the same model, the proposed methods can efficiently crack all key bytes across four considered datasets. Our work, thus, signifies a significant step forward in deep learning-based side-channel attacks, showcasing a high degree of flexibility and efficiency without any presumption of the leakage model.

Side-channel Collision Attacks (SCCA) constitute a subset of non-profiling attacks that exploit information dependency leaked during cryptographic operations. Unlike traditional collision attacks, which seek instances where two different inputs to a cryptographic algorithm yield identical outputs, SCCAs specifically target the internal state, where identical outputs are more likely. In CHES 2023, Staib et al. presented a Deep Learning-based SCCA (DL-SCCA), which enhanced the attack performance while decreasing the required effort for leakage preprocessing. Nevertheless, this method inherits the conventional SCCA's limitations, as it operates on trace segments reflecting the target operation explicitly, leading to issues such as portability and low tolerance to errors.

This paper introduces an end-to-end plaintext-based SCCA to address these challenges. We leverage the bijective relationship between plaintext and secret data to label the leakage measurement with known information, then learn plaintext-based profiling models to depict leakages from varying operations. By comparing the leakage representations produced by the profiling model, an adversary can reveal the key difference. As an end-to-end approach, we propose an error correction scheme to rectify false predictions. Experimental results indicate our approach significantly surpasses DL-SCCA in terms of attack performance (e.g., success rate increased from 53\% to 100\%) and computational complexity (training time reduced from approximately 2 hours to 10 minutes). These findings underscore our method's effectiveness and practicality in real-world attack scenarios.

Profiling side-channel analysis is an essential technique to assess the security of protected cryptographic implementations by subjecting them to the worst-case security analysis. This approach assumes the presence of a highly capable adversary with knowledge of countermeasures and randomness employed by the target device. However, black-box profiling attacks are commonly employed when aiming to emulate real-world scenarios. These attacks leverage deep learning as a prominent alternative since deep neural networks can automatically select points of interest, eliminating the need for secret mask knowledge. Nevertheless, black-box profiling attacks often result in non-worst-case security evaluations, leading to suboptimal profiling models.

In this study, we propose modifying the conventional black-box threat model by incorporating a new assumption: the adversary possesses a similar implementation that can be used as a white-box reference design. We create an adversarial dataset by extracting features or points of interest from this reference design. These features are then utilized for training a novel conditional generative adversarial network (CGAN) framework, enabling a generative model to extract features from high-order leakages in protected implementation without any assumptions about the masking scheme or secret masks. Our framework empowers attackers to perform efficient black-box profiling attack that achieves (and even surpasses) the performance of the worst-case security assessments.

Verifiable timed commitments serve as cryptographic tools that enable the binding of information to specific time intervals. By integrating these commitments into signature schemes, secure and tamper-evident digital signatures can be generated, ensuring the integrity of time-sensitive mechanisms. This article delves into the concept of verifiable timed commitments and explores their efficient applications in digital signature constructions. Specifically, it focuses on two important signature schemes: proxy signatures and multi-signatures. The idea of the timed proxy signature is to enable the delegation of signing rights for a specified period, allowing designated entities to sign messages on behalf of the original signer. On the other hand, multi-signatures allow multiple parties to collectively generate a single signature, ensuring enhanced security and accountability. The article presents an in-depth analysis of the underlying mechanisms, discussing their properties, strengths, and computational complexity. Through this exploration, the article aims to shed light on the potential of verifiable timed commitments and inspire further research in this evolving field of cryptography.

We continue the recent line of work on folding schemes. Building on ideas from ProtoStar [BC23] we construct a folding scheme where the recursive verifier's ``marginal work'', beyond linearly combining witness commitments, consists only of a logarithmic number of field operations and a constant number of hashes. Moreover, our folding scheme performs well when \emph{folding multiple instances at one step}, in which case the marginal number of verifier field operations per instance becomes constant, assuming constant degree gates.

Commodity encrypted storage platforms (e.g., IceDrive, pCloud) permit data store and sharing across multiple users while preserving data confidentiality. However, end-to-end encryption may not be sufficient since it only offers confidentiality when the data is at rest or in transit. Meanwhile, sensitive information can be leaked from metadata representing activities during data operations (e.g., query, processing). Recent encrypted search platforms such as DORY (OSDI’20) or DURASIFT (WPES’19) permit multi-user data query functionalities, while protecting metadata privacy. However, they either incur a high processing overhead or offer limited secu- rity/functionality, and require strong trust assumptions. We propose MAPLE, a new metadata-hiding encrypted search platform that offers query functionalities (search, update) on the shared data across multiple users with complex policy controls. MAPLE protects metadata privacy all the time during query processing, while achieving significantly (asymptotically) lower processing overhead than state-of-the-art platforms. The core technique of MAPLE is the design of oblivious data structures for search index and access control coupled with secure computation techniques to enable efficient query processing with a minimal trust. We fully implemented MAPLE and evaluated its performance on commodity cloud (Amazon EC2) under real settings. Experimental results showed that MAPLE achieved a concrete performance comparable with its counterparts, while offering provably stronger security guarantees and more diverse functionalities.