International Association
for Cryptologic Research

Key Transparency (KT) systems enable service providers of end-to-end encrypted communication (E2EE) platforms to maintain a Verifiable Key Directory (VKD) that maps each user's identifier, such as a username or email address, to their identity public key(s). Users periodically monitor the directory to ensure their own identifier maps to the correct keys, thus detecting any attempt to register a fake key on their behalf to Meddler-in-the-Middle (MitM) their communications. We introduce and formalize a new primitive called Multi-Device Verifiable Key Directory (MVKD), which strengthens both the security, privacy, and usability guarantees of VKD by leveraging the multi-device setting. We formalize three properties for a MVKD (completeness, extraction-based soundness, and privacy), striking a non-trivial balance between strong guarantees and the limitations imposed by a truly practical system. We then present a new MVKD system called ELEKTRA. This system combines the core of the Keybase KT system (running in production since 2014) with ideas from SEEMless (Chase et. al., 2019) and RZKS (Chen et. al., 2022). Our construction is the first to achieve the above multi-device guarantees while having formal security and privacy proofs. Finally, we implement ELEKTRA and present benchmarks demonstrating its practicality.

Recommender systems are effective mechanisms for recommendations about what to watch, read, or taste based on user ratings about experienced products or services. To achieve higher quality recommendations, e-commerce parties may prefer to collaborate over partitioned data. Due to privacy issues, they might hesitate to work in pairs and some solutions motivate them to collaborate. This study examines how to estimate trust-based predictions on arbitrarily partitioned data in which two parties have ratings for similar sets of customers and items. A privacy- preserving scheme is proposed, and it is justified that it efficiently offers trust-based predictions on partitioned data while preserving privacy.

SAT, SMT, MILP, and CP, have become prominent in the differential cryptanalysis of cryptographic primitives. In this paper, we review the techniques for constructing differential characteristic search models in these four formalisms. Additionally, we perform a systematic comparison encompassing over 20 cryptographic primitives and 16 solvers, on both easy and hard instances of optimisation, enumeration and differential probability estimation problems.

Private Set Intersection (PSI) is a widely used protocol that enables two parties to securely compute a function over the intersected part of their shared datasets and has been a significant research focus over the years. However, recent studies have highlighted its vulnerability to Set Membership Inference Attacks (SMIA), where an adversary might deduce an individual's membership by invoking multiple PSI protocols. This presents a considerable risk, even in the most stringent versions of PSI, which only return the cardinality of the intersection. This paper explores the evaluation of anonymity within the PSI context. Initially, we highlight the reasons why existing works fall short in measuring privacy leakage, and subsequently propose two attack strategies that address these deficiencies. Furthermore, we provide theoretical guarantees on the performance of our proposed methods. In addition to these, we illustrate how the integration of auxiliary information, such as the sum of payloads associated with members of the intersection (PSI-SUM), can enhance attack efficiency. We conducted a comprehensive performance evaluation of various attack strategies proposed utilizing two real datasets. Our findings indicate that the methods we propose markedly enhance attack efficiency when contrasted with previous research endeavors. The effective attacking implies that depending solely on existing PSI protocols may not provide an adequate level of privacy assurance. It is recommended to combine privacy-enhancing technologies synergistically to enhance privacy protection even further.

A cipher scheme related to ChaCha with the variation of using 64 bit operations instead of 32 bits, and the same 512 bit state size, is presented. We will provide strong argumentation to assert that the same security of ChaCha can be obtained with half number of instructions for the same number of 20 rounds. Also, an strategy to implement this cipher on SIMD extensions is presented, with a maximal throughput of about 3.37 bytes per cycle on a 256 bit SIMD extension with at least 11 vector registers.

Laconic cryptography enables secure two-party computation (2PC) on unbalanced inputs with asymptotically-optimal communication in just two rounds of communication. In particular, the receiver (who sends the first-round message) holds a long input and the sender (who sends the second-round message) holds a short input, and the size of their communication to securely compute a function on their joint inputs only grows with the size of the sender's input and is independent of the receiver's input size. The work on laconic oblivious transfer (OT) [Cho et al. CRYPTO 2017] and laconic private set intersection (PSI) [Alamati et al. TCC 2021] shows how to achieve secure laconic computation for OT and PSI from the Diffie-Hellman assumption.

In this work, we push the limits further and achieve laconic branching programs from the Diffie-Hellman assumption. In particular, the receiver holds a large branching program $P$ and the sender holds a short input $x$. We present a two-round 2PC protocol that allows the receiver to learn $x$ iff $P(x) =1$, and nothing else. The communication only grows with the size of $x$ and the depth of $P$, and does not further depend on the size of $P$.

In a seminal work, Ishai et al. (FOCS–2006) studied the viability of designing unconditionally secure protocols for key agreement and secure multi-party computation (MPC) using an anonymous bulletin board (ABB) as a building block. While their results establish the feasibility of key agreement and honest-majority MPC in the ABB model, the optimality of protocols with respect to their round and communication complexity is not studied. This paper enriches this study of unconditional security in the ABB model in multiple ways.

- We present a key agreement protocol with a novel combinatorial insight to offer a 200% throughput over the (FOCS–2006) study; i.e., using the same number of messages, we can (almost) double the bit-length of the agreed key. We also prove the near optimality of our approach.

- We offer unconditionally secure protocols for the (random) string oblivious transfer functionalities. We present a $1$-round chosen message random string oblivious transfer and show how to extend it to a non-interactive (random) string oblivious transfer protocol and a $2$-round chosen message string oblivious transfer.

- We prove a $1$-round lower bound for BEC under certain conditions.

Central to our technical contributions is the abstraction of a distributional variant of the random ABB functionality. Investigating the concrete efficiency of founding MPC from this primitive leads to fascinating new mathematical challenges in well-established MPC models, which will be of broader interest to the community.

Despite decades of effort, a stubborn chasm exists between the theory and practice of device-level biometric authentication. Deployed authentication algorithms rely on data that leaks private information about the biometric; thus systems rely on externalized security measures such as trusted execution environments. In particular, the authentication algorithms themselves provide no cryptographic security guarantees.

This is particularly frustrating given the long line of research that has developed theoretical tools---known as fuzzy extractors---that enable secure, privacy preserving biometric authentication with public enrollment data. Unfortunately, the best known constructions involving these rigorous tools can only provide substantial true accept rates with an estimated security of $32$ bits for the iris (Simhadri et al., ISC 2019) and 45 bits for the face (Zhang, Cui, and Yu, ePrint 2021/1559).

This work introduces FiveEyes, an iris key derivation system that integrates an improved feature extractor with a fuzzy extractor that leverages a new mechanism, which we formally analyze, for selecting verification subsets based on statistics of the iris. (These statistics are computed from a class disjoint dataset from our test set.) We present various parameter regimes in order to highlight different true accept rates: 1. $65$ bits of security (equivalent to $87$ bits with a password) at $12\%$ true accept rate, and 2. $50$ bits of security (equivalent to $72$ bits with a password) at $45\%$ true accept rate. We remark that powerful techniques are known that amplify true accept rates (Davida et al., IEEE S&P 1998); in particular, for the first time these results indicate practical viability of biometric authentication with strongcryptographic security.

The use of zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARK) and similar types of proofs has become increasingly popular as a solution for improving scalability, privacy, and interoperability of blockchain systems. However, even with the most advanced proving systems, verifying a single SNARK proof can require a significant amount of computational resources making it expensive to be performed on-chain. This becomes a noticeable bottleneck in scaling SNARK-based applications.

Further efficiency improvement to avoid this bottleneck lies in utilizing distributed recursive proof composition to aggregate multiple existing proofs into one that verifies all underlying proofs.

Building upon this concept, we present a new protocol for decentralized recursive proof aggregation allowing one unique proof to aggregate many input proofs to be efficiently verified on-chain, increasing the throughput and cost efficiency of SNARK-based blockchains. The protocol is designed for decentralized environments where independent actors (provers) can join and contribute to the proof generation process. We also present an incentive scheme for such actors. The protocol is abstract enough to be used with a variety of proving systems that support recursive aggregation.

Event date: 16 December to 20 December 2024
Submission deadline: 10 July 2024
Notification: 10 September 2024

The Security and Privacy Research Unit at TU Wien is offering a fully funded PhD position under the supervision of Dr. Zeta (Georgia) Avarikioti and Univ.-Prof. Dr. Matteo Maffei. If you are interested, please apply at https://tools.spycode.at/recruiting/call/4.

Closing date for applications:

Contact: Zeta Avarikioti and Mattero Maffei

More information: https://tools.spycode.at/recruiting/call/4

We are looking for motivated postdoctoral candidates with experience using zero-knowledge proofs in cutting edge applications and hardware acceleration. The candidate will be working with the Adaptive Computing and Embedded Systems (ACES) Lab at UC San Diego, led by Farinaz Koushanfar. In particular, the student will be working on incorporating zero-knowledge proofs into emerging learning paradigms. Alongside this, the candidate should have experience with GPU/FPGA acceleration, as they will be collaborating with senior PhD students to build an end-to-end zero-knowledge proof accelerator. This position is fully-funded.

Requirements:

• Ph.D. in Computer Engineering, Computer Science, or a closely related field
• Strong ability in at least C/C++ or Rust
• Familiarity with popular open-source zero-knowledge proof frameworks
• Publication record in top venues, with proven research record around zero-knowledge proofs
• Strong theoretical understanding of zero-knowledge proofs and its various constructions
• Ability to work on-site in San Diego

To apply, please send your CV to Farinaz Koushanfar at the email: fkoushanfar@ucsd.edu

Closing date for applications:

Contact: Farinaz Koushanfar (fkoushanfar@ucsd.edu)

We are looking for motivated postdoctoral candidates with experience in applied cryptography and privacy-preserving computation. The candidate will be working with the Adaptive Computing and Embedded Systems (ACES) Lab at UC San Diego, led by Farinaz Koushanfar. In particular, the student will be working at the intersection of computational healthcare applications and privacy-preserving computation. The researcher will be given the freedom to analyze different privacy-preserving protocols and collaborate with medical professionals to create innovative solutions. This position is fully-funded.

Requirements:

• Ph.D. in Computer Science, Computer Engineering, or a closely related field
• Strong ability in at least C/C++, Python, or Rust
• Familiarity with popular open-source privacy-preserving computation frameworks
• Publication record in top venues, with proven research record in applied cryptography or adjacent field
• Strong applied cryptography skills
• Ability to work on-site in San Diego

To apply, please send your CV to Farinaz Koushanfar at the email: fkoushanfar@ucsd.edu

Closing date for applications:

Contact: Farinaz Koushanfar (fkoushanfar@ucsd.edu)

Software Security Researcher Who We Are The Cryptography Research Center (CRC) brings together theoretical and applied cryptographers to contribute to the proliferation of this ever-evolving ecosystem. Our world-class cryptography experts collaborate with key industry players to offer advanced solutions to address the threats faced by today’s digital societies. CRC is part of the Technology Innovation Institute (TII), a global scientific research center attracting the world’s foremost scientists and researchers. TII leads worldwide advances in artificial intelligence, autonomous robotics, quantum computing, cryptography and quantum communications, directed energy, secure communication, smart devices, advanced materials, and propulsion and space technologies, and biotechnology fields. What We Do We design, analyze, and implement cryptographic algorithms and protocols using in-depth technical expertise that encompasses fundamental classical and post-quantum cryptography research, applied cryptography engineering, and research on theoretical and practical cryptanalytic techniques. Responsibilities Participate in security evaluations of in-house and 3rd-party developed products Conduct R&D activities in the areas of vulnerability research, reverse engineering, and exploit development/mitigation bypass Required skills BSc/MSc in Computer Engineering, Computer Science, or related Significant hands-on experience doing reverse engineering of ARM/AARCH64/RISC-V binaries using IDA Pro or Ghidra Hands-on experience with fuzzing (AFL, FuzzTest/centipede) and debugging tools (GDB) Experience performing source code reviews of large code bases Experience with advanced exploitation techniques Proficient with C/C++ and Python Nice to have skills PhD degree in software security or related Proven experience in security/vulnerability research (e.g., papers, CVEs for RCE/LPE)

Closing date for applications:

Contact: mohammed.hannan@tii.ae

Who We Are The Cryptography Research Center (CRC) brings together theoretical and applied cryptographers to contribute to the proliferation of this ever-evolving ecosystem. Our world-class cryptography experts collaborate with key industry players to offer advanced solutions to address the threats faced by today’s digital societies. CRC is part of the Technology Innovation Institute (TII), a global scientific research center attracting the world’s foremost scientists and researchers. TII leads worldwide advances in artificial intelligence, autonomous robotics, quantum computing, cryptography and quantum communications, directed energy, secure communication, smart devices, advanced materials, and propulsion and space technologies, and biotechnology fields. What We Do We design, analyze, and implement cryptographic algorithms and protocols using in-depth technical expertise that encompasses fundamental classical and post-quantum cryptography research, applied cryptography engineering, and research on theoretical and practical cryptanalytic techniques. Responsibilities Participate in security evaluations of in-house and 3rd-party developed products Conduct R&D activities in the areas of vulnerability research, hardware security, side-channel analysis, and fault injection attacks Required skills BSc/MSc in Computer Engineering, Electrical Engineering, or related Significant hands-on experience performing side-channel analysis and/or fault injection attacks on real-world devices Good understanding of system-level security building blocks (e.g., TEE, secure boot, OTP fuses, secure elements) Familiarity with the most common countermeasures found on modern secure chips (e.g., shields, sensors, codes, masking) Experience with C/C++ and Python Nice to have skills PhD degree in hardware security or related Proven experience in security research (e.g., papers, CVEs)

Closing date for applications:

Contact: mohammed.hannan@tii.ae

A Ph.D. position is available in the Cryptology and Data Security research group at the Institute of Computer Science, University of Bern, led by Christian Cachin.

Our research addresses all aspects of security in distributed systems, especially cryptographic protocols, consistency, consensus, and cloud-computing security. We are particularly interested in blockchains, distributed ledger technology, cryptocurrencies, and their security and economics. To learn more about our research topics, please explore https://crypto.unibe.ch. We are part of IC3: The Initiative for Cryptocurrencies and Contracts (http://www.initc3.org>).

Candidates should have a strong background in computer science and its mathematical foundations. They should like conceptual, rigorous thinking for working theoretically. Demonstrated expertise in cryptography, distributed computing, or blockchain technology is a plus. Applicants must hold a master degree in the relevant research fields.

Positions are available for starting in Spring 2024 and come with a competitive salary. The selection process runs until suitable candidates have been found. The University of Bern conducts excellent research and lives up its vision that "Knowledge generates value". The city of Bern lies in the center of Switzerland and offers some of the highest quality of life worldwide.

If you are interested, please apply be sending email with **one single PDF file** and **subject line** set to **Application for Ph.D.**, addressed directly to Prof. Christian Cachin at **crypto.inf (at) unibe.ch.**.

Since we receive many applications, we encourage you to include material that explains your interests, demonstrates your strengths, and sets you apart from others.

Closing date for applications:

Contact: Christian Cachin, https://crypto.unibe.ch/cc/

More information: https://crypto.unibe.ch/jobs/

In this article, we examine Differential Fault Attacks (DFA) targeting two stream ciphers, FLIP and FiLIP. We explore the fault model where an adversary flips a single bit of the key at an unknown position. Our analysis involves establishing complexity bounds for these attacks, contingent upon the cryptographic parameters of the Boolean functions employed as filters and the key size. Initially, we demonstrate how the concept of sensitivity enables the detection of the fault position using only a few keystream bits. This represents an enhancement over previous DFA methodologies applied to these ciphers. Subsequently, we leverage the properties of the filter's derivatives to execute attacks. This approach is universally applicable to any filter, and we delineate specific attack strategies for the two function families previously implemented in these ciphers.

Post-quantum digital signature schemes have recently received increased attention due to the NIST standardization project for additional signatures. MPC-in-the-Head and VOLE-in-the-Head are general techniques for constructing such signatures from zero-knowledge proof systems. A common theme between the two is an all-but-one vector commitment scheme which internally uses GGM trees. This primitive is responsible for a significant part of the computational time during signing and verification.

A more efficient technique for constructing GGM trees is the half-tree technique, introduced by Guo et al. (Eurocrypt 2023). Our work builds an all-but-one vector commitment scheme from the half-tree technique, and further generalizes it to an all-but-\(\tau\) vector commitment scheme. Crucially, our work avoids the use of the random oracle assumption in an important step, which means our binding proof is non-trivial and instead relies on the random permutation oracle. Since this oracle can be instantiated using fixed-key AES which has hardware support, we achieve faster signing and verification times.

We integrate our vector commitment scheme into FAEST (faest.info), a round one candidate in the NIST standardization process, and demonstrates its performance with a prototype implementation. For \(\lambda = 128\), our experimental results show a nearly \(3.5\)-fold improvement in signing and verification times.

SNOVA is a multivariate signature scheme submitted to the ad- ditional NIST PQC standardization project started in 2022. SNOVA is con- structed by incorporating the structure of the matrix ring over a finite field into the UOV signature scheme, and the core part of its public key is the UOV public key whose coefficients consist of matrices. As a result, SNOVA dramatically reduces the public key size compared to UOV. In this paper, we recall the construction of SNOVA, and reconsider its security analysis. In particular, we investigate key recovery attacks applied to the core part of the public key of SNOVA in detail. Due to our analysis, we show that some pa- rameters of SNOVA submitted in the additional NIST PQC standardization do not satisfy the claimed security levels.

The remarkable performance capabilities of AI accelerators offer promising opportunities for accelerating cryptographic algorithms, particularly in the context of lattice-based cryptography. However, current approaches to leveraging AI accelerators often remain at a rudimentary level of implementation, overlooking the intricate internal mechanisms of these devices. Consequently, a significant number of computational resources is underutilized.

In this paper, we present a comprehensive exploration of NVIDIA Tensor Cores and introduce a novel framework tailored specifically for Kyber. Firstly, we propose two innovative approaches that efficiently break down Kyber's NTT into iterative matrix multiplications, resulting in approximately a 75% reduction in costs compared to the state-of-the-art scanning-based methods.Secondly, by reversing the internal mechanisms, we precisely manipulate the internal resources of Tensor Cores using assembly-level code instead of inefficient standard interfaces, eliminating memory accesses and redundant function calls. Finally, building upon our highly optimized NTT, we provide a complete implementation for all parameter sets of Kyber. Our implementation surpasses the state-of-the-art Tensor Core based work, achieving remarkable speed-ups of 1.93x, 1.65x, 1.22x and 3.55x for polyvec_ntt, KeyGen, Enc and Dec in Kyber-1024, respectively. Even when considering execution latency, our throughput-oriented full Kyber implementation maintains an acceptable execution latency. For instance, the execution latency ranges from 1.02 to 5.68 milliseconds for Kyber-1024 on R3080 when achieving the peak throughput.