International Association
for Cryptologic Research

My research group at the Department of Electrical and Computer Engineering (ECE) at the University at Albany, SUNY, is hiring Ph.D. students on the Security of AI Hardware. Responsibilities: We are seeking students who are passionate about research, motivated to explore new ideas, and willing to work in a team environment. You will be expected to work diligently, communicate your results in writing, and publish research papers in top conferences/journals in the field of hardware security. Qualifications: A strong background in one or more of the following topics is required: linear algebra, probability theory, cryptography, or digital hardware design. Prior experience with Verilog hardware description language (HDL), electronic design automation (EDA) tools for application-specific integrated circuit (ASIC) design, or/and field programmable gate arrays (FPGAs) is preferred. The candidate is expected to have excellent verbal and written communication skills. If you're interested, please reach out to me (spotluri@albany.edu) with your resume and transcripts.

Closing date for applications:

Contact: Dr. Seetal Potluri

More information: https://www.seetalpotluri.com/

Multiple positions for PhD students are available at the Institute of Cybersecurity and Cryptology at the University of Wollongong. Specifically, we are seeking for a PhD student who is interested to work in the area of "secure blockchain". The topic in the area of key-evolving signatures, proof of stake and blockchain, algorithm and security proofs, and will be expected to contribute to the research in key-evolving signatures and the applications in POS blockchain. If you are interested, please send your CV to: ic2.uow.scholarship@gmail.com. We are also seeking for a PhD student to work in the topic of "Privacy-Preserving Information Linkage". The successful candidate will spend some time at the University of Surrey, London during their candidature. If you are interested with this topic, please contact Dr Khoa Nguyen (khoa at uow dot edu dot au). These positions will be filled on the first come first served basis.

Closing date for applications:

Contact: Prof Willy Susilo

We are looking for a postdoctoral research fellow (aka associate research fellow) to work in the topic of "secure blockchain". The successful candidate will be proficient with cryptography research, in the area of key-evolving signatures, proof of stake and blockchain, algorithm and security proofs, and will be expected to contribute to the research in key-evolving signatures and the applications in POS blockchain.

Closing date for applications:

Contact: Prof. Willy Susilo

Private Information Retrieval (PIR) permits clients to query entries from a public database hosted on untrusted servers in a privacy-preserving manner. Traditional PIR model suffers from high computation and/or bandwidth cost due to entire database processing for privacy. Recently, Online-Offline PIR (OO-PIR) has been suggested to improve the practicality of PIR, where query-independent materials are precomputed beforehand to accelerate online access. While state-of-the-art OO-PIR schemes (e.g., S&P’24, CRYPTO’23) successfully reduce the online processing overhead to sublinear, they still impose sustainable bandwidth and storage burdens on the client, especially when operating on large databases. In this paper, we propose Pirex, a new OO-PIR scheme with eminent client performance while maintaining the sublinear server processing efficiency. Specifically, Pirex offers clients with sublinear processing, minimal inbound bandwidth, and low storage requirements. Our Pirex design is fairly simple yet efficient, where the majority of operations are naturally low-cost and streamlined (e.g., XOR, PRF, modular arithmetic). We have fully implemented Pirex and evaluated its real-world performance using commodity hardware. Our experimental results demonstrated that Pirex outperforms existing OO-PIR schemes by at least two orders of magnitude. Concretely, with a 1 TB database, Pirex only takes 0.8s to query a 256-KB entry, compared with 30-220s by the state-of-the-art.

Provable privacy typically requires involved analysis and is often associated with unacceptable accuracy loss. While many empirical verification or approximation methods, such as Membership Inference Attacks (MIA) and Differential Privacy Auditing (DPA), have been proposed, these do not offer rigorous privacy guarantees. In this paper, we apply recently-proposed Probably Approximately Correct (PAC) Privacy to give formal, mechanized, simulation-based proofs for a range of practical, black-box algorithms: K-Means, Support Vector Machines (SVM), Principal Component Analysis (PCA) and Random Forests. To provide these proofs, we present a new simulation algorithm that efficiently determines anisotropic noise perturbation required for any given level of privacy. We provide a proof of correctness for this algorithm and demonstrate that anisotropic noise has substantive benefits over isotropic noise.

Stable algorithms are easier to privatize, and we demonstrate privacy amplification resulting from introducing regularization in these algorithms; meaningful privacy guarantees are obtained with small losses in accuracy. We also propose new techniques in order to canonicalize algorithmic output and convert intractable geometric stability verification into efficient deterministic stability verification. Thorough experiments are included, and we validate our provable adversarial inference hardness against state-of-the-art empirical attacks.

We present distributed key generation and decryption protocols for an additively homomorphic cryptosystem based on class groups, improving on a similar system proposed by Braun, Damgård, and Orlandi at CRYPTO '23. Our key generation is similarly constant round but achieves lower communication complexity than the previous work. This improvement is in part the result of relaxing the reconstruction property required of the underlying integer verifiable secret sharing scheme. This eliminates the reliance on potentially costly proofs of knowledge in unknown order groups. We present a new method to batch zero-knowledge proofs in unknown order groups which strengthens these improvements. We also present a protocol which is proven secure against adaptive adversaries in the single inconsistent player (SIP) model. Our protocols are secure in the universal composability (UC) framework and provide guaranteed output delivery. We demonstrate the relative efficiency of our techniques by presenting the running times and communication costs associated with our implementation of the statically secure protocol and provide a direct comparison with alternate state of the art constructions.

Unclonable cryptography utilizes the principles of quantum mechanics to addresses cryptographic tasks that are impossible classically. We introduce a novel unclonable primitive in the context of secret sharing, called unclonable secret sharing (USS). In a USS scheme, there are $n$ shareholders, each holding a share of a classical secret represented as a quantum state. They can recover the secret once all parties (or at least $t$ parties) come together with their shares. Importantly, it should be infeasible to copy their own shares and send the copies to two non-communicating parties, enabling both of them to recover the secret.

Our work initiates a formal investigation into the realm of unclonable secret sharing, shedding light on its implications, constructions, and inherent limitations. ** Connections: We explore the connections between USS and other quantum cryptographic primitives such as unclonable encryption and position verification, showing the difficulties to achieve USS in different scenarios. **Limited Entanglement: In the case where the adversarial shareholders do not share any entanglement or limited entanglement, we demonstrate information-theoretic constructions for USS.

**Large Entanglement: If we allow the adversarial shareholders to have unbounded entanglement resources (and unbounded computation), we prove that unclonable secret sharing is impossible. On the other hand, in the quantum random oracle model where the adversary can only make a bounded polynomial number of queries, we show a construction secure even with unbounded entanglement. Furthermore, even when these adversaries possess only a polynomial amount of entanglement resources, we establish that any unclonable secret sharing scheme with a reconstruction function implementable using Cliffords and logarithmically many T-gates is also unattainable.

The advent of quantum computing technology will compromise many of the current cryptographic algorithms, especially public-key cryptography, which is widely used to protect digital information. Most algorithms on which we depend are used worldwide in components of many different communications, processing, and storage systems. Once access to practical quantum computers becomes available, all public-key algorithms and associated protocols will be vulnerable to criminals, competitors, and other adversaries. It is critical to begin planning for the replacement of hardware, software, and services that use public-key algorithms now so that information is protected from future attacks.” [1]. For this purpose, we have developed a new algorithm that contributes to deal with the aforementioned problem. Instead to use a classical scheme of encoding / decoding methods (keys, prime numbers, etc.), our algorithm is rather based on a combination of functions. Because the cardinality of the set of functions is infinite, it would be impossible for a third party (e.g. a hacker) to decode the secret information transmitted by the sender (Bob) to the receiver (Alice).

This paper presents a generalization of the Learning With Rounding (LWR) problem, initially introduced by Banerjee, Peikert, and Rosen, by applying the perspective of vector quantization. In LWR, noise is induced by rounding each coordinate to the nearest multiple of a fraction, a process inherently tied to scalar quantization. By considering a new variant termed Learning With Quantization (LWQ), we explore large-dimensional fast-decodable lattices with superior quantization properties, aiming to enhance the compression performance over conventional scalar quantization. We identify polar lattices as exemplary structures, effectively transforming LWQ into a problem akin to Learning With Errors (LWE), where the distribution of quantization noise is statistically close to discrete Gaussian. Furthermore, we develop a novel ``quancryption'' scheme for secure source coding. Notably, the scheme achieves near-optimal rate-distortion ratios for bounded rational signal sources, and can be implemented efficiently with quasi-linear time complexity. Python code of the polar-lattice quantizer is available at https://github.com/shx-lyu/PolarQuantizer.

The analysis of the reduction effort of the lattice reduction algorithm is important in estimating the hardness of lattice-based cryptography schemes. Recently many lattice challenge records have been cracked by using the Pnj-BKZ algorithm which is the default lattice reduction algorithm used in G6K, such as the TU Darmstadt LWE and SVP Challenges. However, the previous estimations of the Pnj-BKZ algorithm are simulator algorithms rather than theoretical upper bound analyses. In this work, we present the first dynamic analysis of Pnj-BKZ algorithm. More precisely, our analysis results show that let $L$ is the lattice spanned by $(\mathbf{a}_i)_{i\leq d}$. The shortest vector $\mathbf{b}_1$ output by running $\Omega \left ( \frac{2Jd^2}{\beta(\beta-J)}\left ( \ln_{}{d} +\ln_{} \ln_{}{\max_{i}\frac{\left \| \mathbf{a}_i^{*} \right \| }{(\mathrm{det}L )^{1/d} } } \right ) \right ) $ tours reduction of pnj-BKZ$(\beta,J)$, $\mathbf{b}_1$ satisfied that \memo{$\left \| \mathbf{b}_1 \right \| \le {\gamma}_{\beta}^{\frac{d-1}{2(\beta-J)}+2 } \cdot \left ( \mathrm{det}L \right ) ^{\frac{1}{d} } $}.

Quantum computers can efficiently model and solve several challenging problems for classical computers, raising concerns about potential security reductions in cryptography. NIST is already considering potential quantum attacks in the development of post-quantum cryptography by estimating the quantum resources required for such quantum attacks. In this paper, we present quantum circuits for the NV sieve algorithm to solve the Shortest Vector Problem (SVP), which serves as the security foundation for lattice-based cryptography, achieving a quantum speedup of the square root. Although there has been extensive research on the application of quantum algorithms for lattice-based problems at the theoretical level, specific quantum circuit implementations for them have not been presented yet. Notably, this work demonstrates that the required quantum complexity for the SVP in the lattice of rank 70 and dimension 70 is $2^{43}$ (a product of the total gate count and the total depth) with our optimized quantum implementation of the NV sieve algorithm. This complexity is significantly lower than the NIST post-quantum security standard, where level 1 is $2^{157}$, corresponding to the complexity of Grover's key search for AES-128.

Non-transferability (NT) is a security notion which ensures that credentials are only used by their intended owners. Despite its importance, it has not been formally treated in the context of anonymous tokens (AT) which are lightweight anonymous credentials. In this work, we consider a client who "buys" access tokens which are forbidden to be transferred although anonymously redeemed. We extensively study the trade-offs between privacy (obtained through anonymity) and security in AT through the notion of non-transferability. We formalise new security notions, design a suite of protocols with various flavors of NT, prove their security, and implement the protocols to assess their efficiency. Finally, we study the existing anonymous credentials which offer NT, and show that they cannot automatically be used as AT without security and complexity implications.

This work shows how FALCON can achieve the Beyond UnForgeability Features (BUFF) introduced by Cremers et al. (S&P'21) more efficiently than by applying the generic BUFF transform. Specifically, we show that applying a transform of Pornin and Stern (ACNS'05), dubbed PS-3 transform, already suffices for FALCON to achieve BUFF security. For FALCON, this merely means to include the public key in the hashing step in signature generation and verification, instead of hashing only the nonce and the message; the other signature computation steps and the signature output remain untouched. In comparison to the BUFF transform, which appends a hash value to the final signature, the PS-3 transform therefore achieves shorter signature sizes, without incurring additional computations.

FALCON is candidate for standardization of the new Post Quantum Cryptography (PQC) primitives by the National Institute of Standards and Technology (NIST). However, it remains a challenge to define efficient countermeasures against side-channel attacks (SCA) for this algorithm. FALCON is a lattice-based signature that relies on rational numbers which is unusual in the cryptography field. While recent work proposed a solution to mask the addition and the multiplication, some roadblocks remain, most noticeably how to protect the floor function. We propose in this work to complete the existing first trials of hardening FALCON against SCA. We perform the mathematical proofs of our methods as well as formal security proof in the probing model using the Non-Interference concepts.

Fault Injection (FI) attacks, which involve intentionally introducing faults into a system to cause it to behave in an unintended manner, are widely recognized and pose a significant threat to the security of cryptographic primitives implemented in hardware, making fault tolerance an increasingly critical concern. However, protecting cryptographic hardware primitives securely and efficiently, even with well-established and documented methods such as redundant computation, can be a time-consuming, error-prone, and expertise-demanding task. In this research, we present a comprehensive and fully-automated software solution for the Automated Generation of Fault-Resistant Circuits (AGEFA). Our application employs a generic and extensively researched methodology for the secure integration of countermeasures based on Error-Correcting Codes (ECCs) into cryptographic hardware circuits. Our software tool allows designers without hardware security expertise to develop fault-tolerant hardware circuits with pre-defined correction capabilities under a comprehensive fault adversary model. Moreover, our tool applies to masked designs without violating the masking security requirements, in particular to designs generated by the tool AGEMA. We evaluate the effectiveness of our approach through experiments on various block ciphers and demonstrate its ability to produce fault-tolerant circuits. Additionally, we assess the security of examples generated by AGEFA against Side-Channel Analysis (SCA) and FI using state-of-the-art leakage and fault evaluation tools.

Fully Homomorphic Encryption (FHE) is a transformative technology that enables computations on encrypted data without requiring decryption, promising enhanced data privacy. However, its adoption has been limited due to significant performance overheads. Recent advances include the proposal of domain-specific, highly-parallel hardware accelerators designed to overcome these limitations. This paper introduces PICA, a comprehensive compiler framework designed to simplify the programming of these specialized FHE accelerators and integration with existing FHE libraries. PICA leverages a novel polynomial Instruction Set Architecture (p-ISA), which abstracts polynomial rings and their arithmetic operations, serving as a fundamental data type for the creation of compact, efficient code embracing high-level operations on polynomial rings, referred to as kernels, e.g., encompassing FHE primitives like arithmetic and ciphertext management. We detail a kernel generation framework that translates high-level FHE operations into pseudo-code using p-ISA, and a subsequent tracing framework that incorporates p-ISA functionalities and kernels into established FHE libraries. Additionally, we introduce a mapper to coordinate multiple FHE kernels for optimal application performance on targeted hardware accelerators. Our evaluations demonstrate PICA's efficacy in creation of compact and efficient code, when compared with an x64 architecture. Particularly in managing complex FHE operations such as relinearization, where we observe a 25.24x instruction count reduction even when a large batch size (8192) is taken into account.

We extend the Linicrypt framework for characterizing hash function security as proposed by McQuoid, Swope, and Rosulek (TCC 2018) to support constructions in the ideal cipher model. In this setting, we give a characterization of collision- and second-preimage-resistance in terms of a linear-algebraic condition on Linicrypt programs, and present an efficient algorithm for determining whether a program satisfies the condition. As an application, we consider the case of the block cipherbased hash functions proposed by Preneel, Govaerts, and Vandewall (Crypto 1993), and show that the semantic analysis of PGV given by Black et. al. (J. Crypto. 2010) can be captured as a special case of our characterization. In addition, We model hash functions constructed through the Merkle-Damgård transformation within the Linicrypt framework. Finally, we appy this model to an analysis of how various attacks on the underlying compression functions can compromise the collision resistance of the resulting hash function.

In this work we tackle privacy concerns in biometric verification systems that typically require server-side processing of sensitive data (e.g., fingerprints and Iris Codes). Concretely, we design a solution that allows us to query whether a given Iris Code is similar to one contained in a given database, while all queries and datasets are being protected using secure multiparty computation (MPC). Addressing the substantial performance demands of operational systems like World ID and aid distributions by the Red Cross, we propose new protocols to improve performance by more than three orders of magnitude compared to the recent state-of-the-art system Janus (S&P 24). Our final protocol can achieve a throughput of over a million Iris Code comparisons per second on a single CPU core, while protecting the privacy of both the query and database Iris Codes. We additionally investigate GPU acceleration for some building blocks of our protocol, which results in further speedups of over 38x compared to the respective multi-threaded CPU implementation.

We study selfish mining attacks in longest-chain blockchains like Bitcoin, but where the proof of work is replaced with efficient proof systems -- like proofs of stake or proofs of space -- and consider the problem of computing an optimal selfish mining attack which maximizes expected relative revenue of the adversary, thus minimizing the chain quality. To this end, we propose a novel selfish mining attack that aims to maximize this objective and formally model the attack as a Markov decision process (MDP). We then present a formal analysis procedure which computes an $\epsilon$-tight lower bound on the optimal expected relative revenue in the MDP and a strategy that achieves this $\epsilon$-tight lower bound, where $\epsilon>0$ may be any specified precision. Our analysis is fully automated and provides formal guarantees on the correctness. We evaluate our selfish mining attack and observe that it achieves superior expected relative revenue compared to two considered baselines.

In concurrent work [Sarenche FC'24] does an automated analysis on selfish mining in predictable longest-chain blockchains based on efficient proof systems. Predictable means the randomness for the challenges is fixed for many blocks (as used e.g., in Ouroboros), while we consider unpredictable (Bitcoin-like) chains where the challenge is derived from the previous block.

In recent years, cloud vendors have started to supply paid services for data analysis by providing interfaces of their well-trained neural network models. However, customers lack tools to verify whether outcomes supplied by cloud vendors are correct inferences from particular models, in the face of lazy or malicious vendors. The cryptographic primitive called zero-knowledge proof (ZKP) addresses this problem. It enables the outcomes to be verifiable without leaking information about the models. Unfortunately, existing ZKP schemes for neural networks have high computational overheads, especially for the non-linear layers in neural networks.

In this paper, we propose an efficient and extensible ZKP framework for neural networks. Our work improves the performance of the proofs for non-linear layers. Compared to previous works relying on the technology of bit decomposition, we convert complex non-linear relations into range and exponent relations, which significantly reduces the number of constraints required to prove non-linear layers. Moreover, we adopt a modular design to make our framework compatible with more neural networks. Specifically, we propose two enhanced range and lookup proofs as basic blocks. They are efficient in proving the satisfaction of range and exponent relations. Then, we constrain the correct calculation of primitive non-linear operations using a small number of range and exponent relations. Finally, we build our ZKP framework from the primitive operations to the entire neural networks, offering the flexibility for expansion to various neural networks.

We implement our ZKPs for convolutional and transformer neural networks. The evaluation results show that our work achieves over $168.6\times$ (up to $477.2\times$) speedup for separated non-linear layers and $41.4\times$ speedup for the entire ResNet-101 convolutional neural network, when compared with the state-of-the-art work, Mystique. In addition, our work can prove GPT-2, a transformer neural network with $117$ million parameters, in $287.1$ seconds, achieving $35.7\times$ speedup over ZKML, which is a state-of-the-art work supporting transformer neural networks.