International Association
for Cryptologic Research

Event date: 8 October to 9 October 2025
Submission deadline: 1 July 2025
Notification: 23 August 2025

The Augot-Finiasz system is a public-key encryption (PKE) scheme based on Reed-Solomon codes and was later followed by analogous versions in the rank metric. Although these schemes were eventually broken, their fundamental idea remains exciting. Notably, these schemes are significantly different from the McEliece system as there is no need to hide the code and, as such, promise much better parameters. Further, they admit a simple description where both the public key and ciphertext are just corrupted codewords of a public code. An interesting question is whether the general idea can be made to work, i.e., resist all known attacks, by using other code classes. This paper shows how to generalize the Augot-Finiasz system to other code families. We reduce the correctness and security of this framework to simple assertions about the code class with which it is instantiated. Specifically, its correctness is equivalent to the existence of an efficient error-erasure decoder, and its security reduces to an easily understood hardness assumption, called "supercode decoding", close to the syndrome decoding problem.

In the $k$-Orthogonal Vectors ($k$-OV) problem we are given $k$ sets, each containing $n$ binary vectors of dimension $d=n^{o(1)}$, and our goal is to pick one vector from each set so that at each coordinate at least one vector has a zero. It is a central problem in fine-grained complexity, conjectured to require $n^{k-o(1)}$ time in the worst case.

We propose a way to plant a solution among vectors with i.i.d. $p$-biased entries, for appropriately chosen $p$, so that the planted solution is the unique one. Our conjecture is that the resulting $k$-OV instances still require time $n^{k-o(1)}$ to solve, on average.

Our planted distribution has the property that any subset of strictly less than $k$ vectors has the same marginal distribution as in the model distribution, consisting of i.i.d. $p$-biased random vectors. We use this property to give average-case search-to-decision reductions for $k$-OV.

For very long messages, the reliable broadcast protocol with the best communication complexity to date is the Minicast protocol of Locher & Shoup [2024]. To reliably broadcast a message $m$ to $n$ parties, Minicast has communication complexity $\sim 1.5 |m| n$, when $|m|$ is large. However, the round complexity of Minicast is 4, which is worse than the 3 rounds of the classical protocol of Bracha. We give a new reliable broadcast protocol whose communication complexity is essentially the same as that of Minicast, but whose round complexity is just 3. Like Minicast, our new protocol does not rely on any cryptography other than hash functions. We also give a new 2-round protocol that relies on signatures. For large $|m|$, its communication complexity is also $\sim 1.5 |m| n$, unless the sender provably misbehaves, in which case its communication complexity may degrade to at most $\sim 2 |m| n$.

One-way functions (OWFs) form the foundation of modern cryptography, yet their unconditional existence remains a major open question. In this work, we study this question by exploring its relation to lossy reductions, i.e., reductions $R$ for which it holds that $I(X;R(X)) \ll n$ for all distributions $X$ over inputs of size $n$. Our main result is that either OWFs exist or any lossy reduction for any promise problem $\Pi$ runs in time $2^{\Omega(\log\tau_\Pi / \log\log n)}$, where $\tau_\Pi(n)$ is the infimum of the runtime of all (worst-case) solvers of $\Pi$ on instances of size $n$. More precisely, by having a reduction with a better runtime, for an arbitrary promise problem $\Pi$, and by using a non-uniform advice, we construct (a family of) OWFs. In fact, our result requires a milder condition, that $R$ is lossy for sparse uniform distributions (which we call mild-lossiness). It also extends to $f$-reductions as long as $f$ is a non-constant permutation-invariant Boolean function, which includes $\text{And-, Or-, Maj-, Parity-}$, $\text{Mod}_k$-, and $\text{Threshold}_k$-reductions.

Additionally, we show that worst-case to average-case Karp reductions and randomized encodings are special cases of mild-lossy reductions and improve the runtime above as $2^{\Omega(\log \tau_\Pi)}$ when these mappings are considered. Restricting to weak fine-grained OWFs, this runtime can be further improved as $\Omega(\tau_\Pi)$. Intuitively, the latter asserts that if weak fine-grained OWFs do not exist then any instance randomization of any $\Pi$ has the same runtime (up to a constant factor) as the best worst-case solver of $\Pi$.

Taking $\Pi$ as $k\text{Sat}$, our results provide sufficient conditions under which (fine-grained) OWFs exist assuming the Exponential Time Hypothesis (ETH). Conversely, if (fine-grained) OWFs do not exist, we obtain impossibilities on instance compressions (Harnik and Naor, FOCS 2006) and instance randomizations of $k\text{Sat}$ under the ETH. Moreover, the analysis can be adapted to studying such properties of any $\text{NP}$-complete problem.

Finally, we partially extend these findings to the quantum setting; the existence of a pure quantum mildly-lossy reduction for $\Pi$ within the runtime $2^{o(\log\tau_\Pi / \log\log n)}$ implies the existence of one-way state generators, where $\tau_\Pi$ is defined with respect to quantum solvers.

Fully Homomorphic Encryption (FHE) enables arbitrary and unlimited computations directly on encrypted data. Notably, the TFHE scheme allows users to encrypt bits or small numbers (4-6 bits) and compute any univariate function using programmable bootstrapping (PBS), while simultaneously refreshing the ciphertext noise. Since both linear and non-linear functions can be evaluated using PBS, it is possible to compute arbitrary functions and circuits of unlimited depth without any accuracy loss. Nevertheless, a major limitation of TFHE, compared to other FHE schemes, is that it operates on a single ciphertext at a time, and the underlying message size remains small. For larger messages with longer bit sizes, the execution overhead of PBS grows exponentially with the number of message bits. A recent approach, called Without-padding PBS (WoPBS), enables computation of much larger lookup tables (10-28 bits), with the execution cost scaling linearly with the number of message bits. The significant encoding mismatch between the PBS and WoPBS, however, complicates the use of both approaches within the same circuit execution.

In this work, we introduce novel switching algorithms that enable ciphertexts to be converted back and forth between the PBS and WoPBS contexts without impacting the input noise. Moreover, we introduce a new method to bootstrap ciphertexts within the WoPBS context, allowing for unlimited XOR operations at negligible cost. To enhance runtime, we further introduce optimized parameters for both contexts. We validate our techniques through the homomorphic evaluation of AES encryption and decryption, demonstrating transciphering applications that outperform related works.

This whitepaper introduces Clementine, a secure, collateral-efficient, trust-minimized, and scalable Bitcoin bridge based on BitVM2 that enables withdrawals from rollups or other side systems to Bitcoin. Clementine proposes a new Bitcoin light client that remains secure against adversaries controlling less than 50% of Bitcoin’s hash rate, assuming at least one honest Watchtower in a permissioned set. The protocol is collateral-efficient, reusing locked funds over time and reducing unnecessary dust outputs through the strategic use of 0-value outputs, and scalable, enabling a single challenge per Operator to slash multiple misbehaviors. This increases throughput and reduces on-chain load without compromising security. Clementine enables trust-minimized and efficient peg-outs from Citrea to Bitcoin, making zk-rollups on Bitcoin practical and unlocking new paths for native scalability and interoperability.

We are seeking to recruit a researcher for an interdisciplinary project on notions of "explainability" in the context of side channel evaluations (considering technical approaches used in both FIPS style as well as CC style evaluations).

The project will run up to three years. It will require a mix of technical skills (we wish to propose and evaluate novel approaches to gather evidence for/against security of implementations given access to side channels) as well as an interest in developing social science research methodologies (we plan to engage with evaluation labs but also vendors to research useful notions of "explainable leakage").

The project will be co-supervised by Prof. Elisabeth Oswald and Prof. Katharina Kinder-Kurlanda; both situated in the interdisciplinary Digital Age Research Centre at the University of Klagenfurt (Austria).

We seek applicants with a mathematical/technical background. For applicants wishing to pursue a PhD, we expect that they have done a MSc/Bsc thesis on side channels/faults with a practical focus. For applicants who already possess a PhD, we expect a strong track record in applied cryptography with some publications in the area of side channels/faults in top venues.

The post holder will be expected to work in Klagenfurt (Austria), and to be able to do short term visits to evaluation labs/vendors throughout Europe.

In order to apply, please send a short CV, including your scientific outputs (e.g. papers, talks, seminars, open source artefacts, etc.), as a single pdf file to [email protected]. If you have questions, or wish to discuss informally, please contact Elisabeth Oswald.

We will review applications as they arrive and invite potentially suitable candidates for an online interview as soon as possible, with the intention to fill the post once a suitable candidate has been identified.

Closing date for applications:

Contact: Elisabeth Oswald (Elisabeth.Oswald AT aau.at)

The Young Investigator Group “COSYS - Control Systems and Cyber Security Lab” at the Chair of IT Security at the Brandenburg University of Technology Cottbus-Senftenberg has an open PhD/Postdoc position in the following areas:

AI-based Network Attack Detection and Simulation.

AI-enabled Penetration Testing.

Privacy-Enhancing Technologies in Cyber-Physical Systems.

The available position is funded as 100% TV-L E13 tariff in Germany and limited until 31.07.2026, with possibility for extension. Candidates must hold a Master’s degree (PhD degree for Postdocs) or equivalent in Computer Science or related disciplines, or be close to completing it. If you are interested, please send your CV, transcript of records from your Master studies, and an electronic version of your Master's thesis (if possible), as a single pdf file. Applications will be reviewed until the position is filled.

Closing date for applications:

Contact: Ivan Pryvalov ([email protected])

Postdoctoral Research Position at Shaoxing University Annual Salary: 300,000 RMB Requirements: Candidates must have a background in cybersecurity and smart cities. Submit 3 recent first-authored papers (not from special issues). Include a brief CV. Application: Email applications only to [email protected]. Shortlisted candidates will be notified for further steps. Subject Line: Postdoc Application – Cybersecurity and Smart Cities

Closing date for applications:

Contact: Mehdi Gheisari

We are looking for a motivated and talented PhD student with a passion for reinforcement learning and cybersecurity. Your main objectives will be to contribute to realistic cybersecurity benchmarks and simulators for the RL research community, develop state-of-the-art algorithms that adapt to changing threats, incorporate user models, and provide tangible performance assurance. You will investigate how to represent network environments, implement multi-agent or single-agent RL approaches, and evaluate them using realistic scenarios. Your research may have a direct real societal impact by helping to protect critical infrastructure and everyday technology users. Requirements - M.Sc. degree (completed or near completion) in Computer Science, Cyber Security, Artificial Intelligence, or a related field. - Demonstrated interest and experience (e.g., via projects, code, or publications) in reinforcement learning and/or cybersecurity. - Experience with programming (e.g., in Python, C/C++, or similar) - Proficiency in English (oral and written). - Excellent communication and collaboration skills. Are you interested in this exciting position but still have questions? Feel free to contact Dr. Ashish Sai at [email protected] or Dr. Dennis Soemers at [email protected].

Closing date for applications:

Contact: Dr. Ashish Sai ([email protected]).

More information: https://vacancies.maastrichtuniversity.nl/job/Maastricht-PhD-in-Adaptive-AI-Defense-Reinforcement-Learning-for-Cybersecurity/818657402/

Confidential Inference and Explainability: Toward Self-Diagnosis via Imaging

This PhD topic aims to jointly address privacy and explainability of decisions obtained through image analysis using a neural network. In the context of a classification task performed on a remote server, the goal is to develop approaches that ensure the confidentiality of the explanation as well as that of the input (and output) data. Preserving the privacy of data while ensuring the transparency of the model is a crucial challenge, particularly in domains such as healthcare. The objective aligns with the emerging regulatory framework on AI at the European level (AI Act). While these issues are the subject of significant research individually - whether in applied cryptography or machine learning - the combination of explainability under privacy constraints represents a new research problem. The project will seek to identify local explainability methods based on visual information or concepts that can be adapted to a privacy-preserving mode. Confidentiality may be approached through secure multi-party computation and/or homomorphic encryption. Thanks to a collaboration with the Saint-Etienne University Hospital (France), it will be possible to fine-tune the secure AI system and conduct supervised experiments on health data, aimed at enabling self-diagnosis. The experimentation may also extend to ethical and legal dimensions, through a partnership with the University of Ottawa.

PhD Location: Laboratoire Hubert Curien (LabHC), Université Jean Monnet, Saint-Etienne, France (regular meetings at the CITI Laboratory, INSA Lyon, Villeurbanne, France).

Starting date: 01/10/2025.

Expected profile: Candidates holding a degree from an engineering school or a Master 2 from a university in applied mathematics or computer science, with training in cryptography and machine learning, and proficiency in a programming language and one or more reference development libraries in one of these fields.

Send your CV, cover letter and master transcripts and give contact details of referees by 25/05/2025.

Closing date for applications:

Contact:

Thierry Fournel (LabHC, fournel(at)univ-st-etienne.fr), Clémentine Gritti (CITI, Inria, clementine.gritti(at)insa-lyon.fr) and Amaury Habrard (LabHC, Inria, amaury.habrard(at)univ-st-etienne.fr)

Authenticity-oriented (previously named as privacy-free) garbling schemes of Frederiksen et al. Eurocrypt ’15 are designed to satisfy only the authenticity criterion of Bellare et al. ACM CCS ’12, and to be more efficient compared to full-fledged garbling schemes. In this work, we improve the state-of-the-art authenticity-oriented version of half gates (HG) garbling of Zahur et al. Crypto ’15 by allowing it to be bandwidth-free if any of the input wires of an AND gate is freely settable by the garbler. Our full solution AuthOr then successfully combines the ideas from information-theoretical garbling of Kondi and Patra Crypto ’17 and the HG garbling-based scheme that we obtained. AuthOr has a lower communication cost (i.e. garbled circuit or GC size) than HG garbling without any further security assumption. Theoretically, AuthOr’s GC size reduction over HG garbling lies in the range between 0 to 100%, and the exact improvement depends on the circuit structure. We have implemented our scheme and conducted tests on various circuits that are constructed by independent researchers. Our experimental results show that in practice, the GC size gain may be up to roughly 98%.

We propose BLASter, a proof of concept LLL implementation that demonstrates the practicality of multiple theoretical improvements. The implementation uses the segmentation strategy from Neumaier–Stehlé (ISSAC 2016), parallelism and Seysen's reduction that was proposed by Kirchner–Espitau–Fouque (CRYPTO 2021) and implemented in OptLLL, and the BLAS library for linear algebra operations. It consists of only 1000 significant lines of C++ and Python code, and is made publicly available.

For q-ary lattices that fplll can handle without multiprecision (dimension <180), BLASter is considerably faster than fplll, OptLLL and Ryan–Heninger's flatter (CRYPTO 2023), without degrading output reduction quality. Thanks to Seysen's reduction it can further handle larger dimension without resorting to multiprecision, making it more than 10x faster than flatter and OptLLL, and 100x faster than fplll in dimensions 256 to 1024.

It further includes segmented BKZ and segmented deep-LLL variants. The latter provides bases as good as BKZ-15 and has a runtime that is only a couple of times more than our LLL baseline.

This remains a proof of concept: the effective use of higher precision — which is needed to handle \(\textit{all}\) lattices — has further obstacles and is left for future work. Still, this work contains many lessons learned, and is meant to motivate and guide the development of a robust and modern lattice reduction library, which shall be much faster than fplll.

At CANS 2024, Zbudila et al. presented MaSTer, a maliciously secure multi-party computation protocol for truncation. It allows adversaries to manipulate outputs with a bounded additive error while avoiding detection with a certain probability. In this work, we analyse the broader implications of adversarial exploitation in probabilistic truncation protocols, specifically in relation to MaSTer. We propose three attack strategies aimed at inducing misclassification in deep neural network (DNN) inference. Our empirical evaluation across multiple datasets demonstrates that while adversarial influence remains negligible under realistic constraints, certain configurations and network architectures exhibit increased vulnerability. By improving the understanding of the risks associated with probabilistic truncation protocols in privacy-preserving machine learning, our work demonstrates that the MaSTer protocol is robust in realistic settings.

Generic Secure Multiparty Computation (Generic MPC) recently received much attraction in the blockchain realm as it allows mutually distrustful parties to jointly compute a global function using their private inputs while keeping them private; and more so; the expression of the function can be done in a programmable manner (hence `generic'); as opposed to the first rising star cryptographic technique Zero-Knowledge Proof (ZKP) which only allows computation on private input of a single party (via the `commit-and-prove' approach). While ZKP, by nature, allows public verifiability, Generic MPC is not so: Generic MPC mostly focuses on Malicious Security in which the computing result is verifiable only among the computing parties. Yet, in the blockchain realm, public verifiability is important, as the consensus protocol is not just among the computing parties but also external servers. A few works were done to bridge this gap (albeit not in the blockchain realm), i.e., Public Auditable MPC. Public Audtitability is a stronger property than Public Verifiability: the first one certifies the computation done in the MPC, while the latter certifies only the relation between the outputs and the inputs. However, they are non-constant round protocols and only for Secret-Sharing-based MPC, i.e., round complexity scales linearly with the circuit multiplicative depth, while round latency is an important cost metric in the blockchain domain. We address this problem by providing a Public Auditable Garbled Circuit protocol that is maliciously secure, publicly auditable, and constant-round. Our protocol is efficient, with only minimal overhead in terms of round, communication, and public transcript size.

Differential Fault Attacks (DFAs) have recently emerged as a significant threat against stream ciphers specifically designed for Hybrid Homomorphic Encryption (HHE). In this work, we propose DFAs on the $\textsf{FRAST}$ cipher, which is a cipher specifically tailored for Torus-based Fully Homomorphic Encryption (TFHE). The round function of $\textsf{FRAST}$ employs random S-boxes to minimize the number of rounds, and can be efficiently evaluated in TFHE. With our specific key recovery strategy, we can mount the DFA with a few faults. Under the assumption of precise fault injection, our DFA can recover the key within one second using just 4 or 6 faults. When discarding the assumption and considering a more practical fault model, we can still achieve key recovery in a few minutes without increasing the number of faults. To the best of our knowledge, this is the first third-party cryptanalysis on $\textsf{FRAST}$. We also explored countermeasures to protect $\textsf{FRAST}$. Our analysis revealed that negacyclic S-boxes, a key component of TFHE-friendly ciphers, are unsuitable for incorporating linear structures to resist DFA. Consequently, we recommend removing the negacyclic restriction in the penultimate round of FRAST and introducing non-zero linear structures into the S-boxes of the last two rounds. We believe that our work will provide valuable insights for the design of TFHE-friendly ciphers.

Homomorphic Encryption (HE) allows computations on encrypted data without decryption. It can be used where the users’ information are to be processed by an untrustful server, and has been a popular choice in privacy-preserving applica- tions. However, in order to obtain meaningful results, we have to assume an honest-but-curious server, i.e., it will faithfully follow what was asked to do. If the server is malicious, there is no guarantee that the computed result is correct. The notion of verifiable HE (vHE) is introduced to detect malicious server’s behaviors, but current vHE schemes are either more than four orders of magnitude slower than the underlying HE operations (Atapoor et. al, CIC 2024) or fast but incompatible with server- side private inputs (Chatel et. al, CCS 2024).

In this work, we propose a vHE framework ZHE: effi- cient Zero-Knowledge Proofs (ZKPs) that prove the correct execution of HE evaluations while protecting the server’s private inputs. More precisely, we first design two new highly- efficient ZKPs for modulo operations and (Inverse) Number Theoretic Transforms (NTTs), two of the basic operations of HE evaluations. Then we build a customized ZKP for HE evaluations, which is scalable, enjoys a fast prover time and has a non-interactive online phase. Our ZKP is applicable to all Ring-LWE based HE schemes, such as BGV and CKKS. Finally, we implement our protocols for both BGV and CKKS and conduct extensive experiments on various HE workloads. Compared to the state-of-the-art works, both of our prover time and verifier time are improved; especially, our prover cost is only roughly 27-36× more expensive than the underlying HE operations, this is two to three orders of magnitude cheaper than state-of-the-arts.

We study the problem of how to find the inverse of shift invariant (SI) transformations proposed in Daemen's thesis. In particular, two of them have been used in practice: $y_i=x_i\oplus \overline{x_{i+1}}x_{i+2}$ and $y_i=x_i\oplus \overline{x_{i+1}}x_{i+2}x_{i+3}$. The first one is the well-known $\chi$ transformation used in \textsf{SHA-3}, \textsf{Subterranean 2.0} and \textsf{Rasta}, while the second one is used in a recently proposed ZK-friendly hash function called Monolith. While the concrete formula of the inverse of $\chi$ of arbitrary size has been given and proved by Liu et al. at JoC 2022, it remains unknown how to deduce such a formula and how to systematically study other SI transformations. In this work, we aim to provide a general method and flow to find the inverse of SI transformations, though it is still limited to some specific types and it may not work for all such transformations. However, such a general method does shed new insight on how to find their inverse, as we can apply this method to several different SI transformations, including the one used in Monolith. We expect that this method can be further generalized and applied to more SI transformations.

Lattices are the basis of most NIST-recommended post-quantum cryptography (PQC) schemes, required to thwart the threat posed by the eventual construction of large-scale quantum computers. At the same time, lattices enable more advanced cryptographic constructions, such as fully homomorphic encryption (FHE), which is increasingly used for privacy-preserving applications like machine learning. This work delves into the efficiency and trade-off assessment of polynomial multiplication algorithms and their applications to PQC, FHE, and other schemes. Such algorithms are at the core of lattice-based cryptography and may become a critical bottleneck when deploying PQC- and FHE-based solutions on resource-constrained devices. We propose a formal analysis of so-called incompleteness in the Number Theoretic Transform (NTT). Although this concept is not new, our systematization shows how to optimize polynomial multiplication in quotient rings, considering factors such as the degree of incompleteness, the associated prime moduli, constraints of the target platform, and target security level. Besides efficiency, we formally show that the systematized family of incomplete NTT variants supports a larger set of prime moduli. This property enables new trade-offs for algorithms like the FIPS-approved module-lattice-based key encapsulation mechanism (ML-KEM) and faster amortized bootstrapping in FHE schemes. Our results include shorter ciphertexts in ML-KEM with only a modest hit in performance and a 6-42% performance boost in the NTT computation of a state-of-the-art FHE solution.