International Association
for Cryptologic Research

The decentralisation of autonomous Unmanned Aircraft Systems (UAS) introduces significant challenges for establishing secure communication and consensus in contested, resource-constrained environments. This research addresses these challenges by conducting a comprehensive performance evaluation of two cryptographic technologies: Messaging Layer Security (MLS) for group key exchange, and threshold signatures (FROST and BLS) for decentralised consensus. Seven leading open-source libraries were methodically assessed through a series of static, network-simulated, and novel bulk-signing benchmarks to measure their computational efficiency and practical resilience. This paper confirms that MLS is a viable solution, capable of supporting the group sizes and throughput requirements of a UAS swarm. It corroborates prior work by identifying the Cisco MLSpp library as unsuitable for dynamic environments due to poorly scaling group management functions, while demonstrating that OpenMLS is a highly performant and scalable alternative. Furthermore, the findings show that operating MLS in a 'Key Management' mode offers a dramatic increase in performance and resilience, a critical trade-off for UAS operations. For consensus, the benchmarks reveal a range of compromises for developers to consider, while identifying the Zcash FROST implementation as the most effective all-around performer for sustained, high-volume use cases due to its balance of security features and efficient verification.

A Decentralized Autonomous Organization (DAO) is a pioneering evolution to realize a decentralized democratic governance over a blockchain. In a DAO, stakeholders usually make collective decisions through secure on-chain voting. Recently, Dark DAO (Austgen et al., arXiv:2311.03530) was proposed as a decentralized cartel that enables automated vote-buying. It attacks the inalienable authentication of a remote e-voting system by leveraging key encumbrance via MPC or TEEs, enabling a voter to pass the authentication without knowing the actual key. To defend against this new type of attack, the notions of individual knowledge (Dziembowski et al., CRYPTO '23) and complete knowledge (Kelkar et al., CCS '24) were proposed, ensuring that the prover has unencumbered knowledge of a secret. However, their solutions rely on TEEs or ASICs, which are difficult to deploy on blockchain.

Inspired by the human-extractable CAPTCHA puzzles proposed by Kumarasubramanian et al. (PKC '13), we propose a new primitive called human-extractable zero-knowledge proofs of knowledge (HE-ZKPoK) as an alternative solution to Dark DAOs. Our HE-ZKPoK protocol forces the prover to solve human-extractable CAPTCHA puzzles along with completing a standard zero-knowledge proof of knowledge, avoiding the need for specialized hardware. As a result, any human entity can extract the witness merely by looking at the prover's CAPTCHA queries and the associated puzzles. Under the assumption that humans cannot encumber a secret, we conclude that if a voter sells his vote, his secret key will be fully exposed, thus deterring voters from engaging in vote-buying.

Data regulations grant users the right to be forgotten, empowering them to control if and when their data is used in applications such as machine learning training. Machine unlearning offers a promising mechanism to enforce this right by enabling the removal of specific training data from models. Existing machine unlearning approaches, however, assume an honest server that correctly executes all unlearning requests. In practice, this assumption is too strong: nothing prevents a server from falsely claiming to have performed unlearning while secretly retaining the original model or continuing to use the data for training. Such behaviours remain possible even when unlearning requests are verifiable---for example, via zero-knowledge proofs---because the server may still keep copies of the data or model.

In this work, we argue that a security model for machine unlearning should capture data confidentiality throughout the lifecycle of a model, including training, inference, and unlearning. We introduce such a formalism and then present the first machine learning framework that provides cryptographic guarantees that unlearning requests are properly executed and that users' data is forgotten. We implement our framework using fully-homomorphic encryption (FHE) and secure multi-party computation (MPC), within a distributed setting where training, unlearning and inference requests are handled by a group of servers. Our constructions are secure in the honest-but-curious model if at least one of the servers is honest, and can be lifted against actively malicious servers following standard techniques. We also show, via a proof-of-concept implementation, that such a system does not add a significant overhead on top of FHE-based training.

Achieving proactive security in perfectly-secure Multi-Party Computation (MPC) with guaranteed output delivery is a significant challenge, primarily because traditional protocols require all participants to be continuously online, rendering them impractical for many applications. The recently proposed layered MPC model~\cite{C:DDGIKK23} addresses this by allowing parties to be offline for extended periods. However, existing protocols for this model incur substantial overhead compared to their counterparts in the standard static setting.

This work introduces a unified framework and essential building blocks for constructing protocols in the layered model, instantiable with both Shamir and CNF secret sharing. Using this framework, we develop highly efficient protocols for Verifiable Secret Sharing (VSS) and secure multiplication for proactive security.

Applying our framework, we construct layered MPC protocols that drastically reduce the communication complexity and the number of layers required to evaluate an arithmetic circuit of depth $D$. Specifically, our Shamir-based MPC achieves $O(n^6)$ per-gate communication with a total layer depth of $D+13$, representing a significant improvement over the $O(n^9)$ complexity and $10D+8$ depth of~\cite{C:DDGIKK23}.

Schnorr blind signature is one of the most efficient and widely used blind signatures. At CRYPTO'23, Katsumata et al. proposed CSI-Otter, the first blind signature from isogenies, which does not follow the construction framework of the Schnorr blind signature. Instead, CSIOtter was constructed from the sigma protocol for an OR relation that captures the idea of the Abe-Okamoto signature and hence can adapt the proof techniques by Kastner, Loss and Xu (PKC'22) into its security proof. Unfortunately, the concurrent security of CSIOtter was later broken independently by Katsumata et al. (PKC'24) and Do et al. (Eurocrypt'24). As a result, CSI-Otter and all Schnorr-like blind signature schemes constructed from sigma protocols with small challenge space are limited to the sequential setting, though it is still a very meaningful security guarantee.

In this paper, we provide an intensive study of the Schnorr blind signature from isogenies in the Algebraic Group Action Model (AGAM) and the Random Oracle Model (ROM). In particular, we first prove the tight security of the existing Schnorr signature from isogenies under the group action discrete logarithm assumption (GADLOG) in AGAM + ROM, which serves as the foundation for the proof of the sequential security, the log-concurrent security, and the 2-concurrent security of the Schnorr blind signature in AGAM + ROM under the hardness of the one-more group action discrete logarithm (OMGADLOG) assumption. In addition, of independent interest, we also present the Schnorr-Signed Hashed ElGamal KEM from isogenies and prove its CCA2 security in AGAM + ROM under the hardness of GADLOG.

We present protocols for server-aided private set union (PSU), third-party private set difference (TP-PSD) and third-party private symmetric difference (TP-PSymD). In a third-party setting, the receiver who obtains the output is an external inputless party with two other participating input parties. The protocols for third-party private set operations presented in this work are significantly more efficient than that of Yeo and Ying (USENIX ’25). Our results improve upon the above work in both the computational complexity and practical performances. Moreover, our protocols demonstrate practical gains by achieving substantially quicker running times as well as the ability to run on much larger sets. Our server-aided private set union protocol is several times faster than existing state-of-the-art two-party private set union protocols.

We construct unclonable encryption (UE) in the Haar random oracle model, where all parties have query access to $U,U^\dagger,U^*,U^T$ for a Haar random unitary $U$. Our scheme satisfies the standard notion of unclonable indistinguishability security, supports reuse of the secret key, and can encrypt arbitrary-length messages. That is, we give the first evidence that (reusable) UE, which requires computational assumptions, exists in ``micocrypt'', a world where one-way functions may not exist. As one of our central technical contributions, we build on the recently introduced path recording framework to prove a natural ``unitary reprogramming lemma'', which may be of independent interest.

Privacy-Preserving Federated Learning (PPFL) emphasizes the security and privacy of contributors' data in scenarios such as healthcare, smart grids, and the Internet of Things. However, ensuring the security and privacy throughout PPFL can be challenging, given the complexities of maintaining relationships with many users across multiple epochs. Additionally, under a threat model in which the aggregating server and corrupted users are colluding adversaries, honest users' inputs and output data must be protected at all stages. Two common tools for enforcing privacy in federated learning are Private Stream Aggregation (PSA) and Trusted Execution Environments (TEE). However, PSA-only approaches still expose the raw aggregate to the server (and thus to colluding parties). TEE-only aggregation typically incurs non-negligible per-client per-epoch overhead at scale because the TEE must handle per-client communication and maintain per-client state/key material. This paper presents SCALE-FL, a novel solution for PPFL that maintains security while achieving near-plaintext performance using a state-of-the-art PSA protocol to collect user information and a TEE to hide information about the raw aggregate. By using a PSA protocol for aggregation, we can maintain the privacy of information on the untrusted server without requiring per-user key storage or use by the TEE. Then, the aggregate is securely processed by the TEE in plaintext, without the heavy encryption required on an untrusted server. Finally, we ensure the security of user inputs in the federated learning output by using Differential Privacy (DP). The additional overhead introduced by SCALE-FL is 1% of the overhead of the plain FL executions.

Compression has been a fundamental problem in computer science for decades. Simply put, we want to represent a low-entropy vector $v$ of size $n$ with less than $n$ elements so that $v$ can be reconstructed (decompressed) from the shorter representation. Since compressed vectors require less storage and less communication, compression algorithms are part of almost every digital system.

When the vector is encrypted with fully homomorphic encryption (FHE) the problem becomes significantly harder. Some research (e.g., [TCHES'19, CCS'21, EuroCrypt'23 ,USENIX'24]) have considered the problem of compressing an encrypted vector but they all assumed the decompression step happens in cleartext. This is a significant restriction. For example, any system with an untrusted agent that needs to receive data and analyze it cannot use existing compression algorithms.

In this paper, we give the first (to the best of our knowledge) non-trivial compression-decompression algorithms that are both FHE-friendly. Our algorithms use the copy-and-recurse technique together with the known duality between compression and error-correcting codes. Our experiments show that our decompression algorithm is faster than the folklore decompression algorithm. This is useful in systems with an agent-in-the-middle that is bounded by communication and by computation.

Secure Neural Network Inference (SNNI) enables privacy-preserving inference on encrypted data with strong cryptographic guarantees. However, practical deployments suffer from high preprocessing overhead, significant communication costs, and sequential execution. These limitations lead to low throughput, underutilized system resources, long queueing delays, and poor scalability.

This work introduces \textit{SwiftSNNI}, a unified, resource-aware scheduling framework for SNNI. It implements a hybrid offline–online strategy that orchestrates offline preprocessing ($T_{\text{pre}, i}$) and online inference ($T_{\text{on}, i}$) jobs to maximize parallelism. By formulating SNNI scheduling as a constrained optimization problem, \textit{SwiftSNNI} overlaps $T_{\text{pre, i}}$ phase execution of future requests with active $T_{\text{on, j}}$ jobs. \textit{SwiftSNNI} also incorporates optional advance notices to enable proactive $T_{\text{pre}, i}$, which further reduces average input delay ($D$).

Evaluations using five benchmark neural networks (M1, M2, HiNet, AlexNet, VGG-16) under diverse workloads and stochastic arrival rates confirm substantial performance gains. Compared to a parallelized sequential baseline (MS-SHARK), \textit{SwiftSNNI} achieves up to 97\% lower average input delay ($D$), a 81\% reduction in makespan ($\approx 5.4 \times$ speedup), and delivers $5.6 \times$ increase in throughput. Furthermore, \textit{SwiftSNNI} reduces average waiting time ($W$) by over 99\%, demonstrating robust starvation prevention for high-concurrency workloads. \textit{SwiftSNNI} supports concurrent execution, scales to larger neural networks, and provides an efficient runtime for SNNI deployments. The \footnote{https://github.com/KanwalBat00l/SwiftSNNI}{\textit{SwiftSNNI}} implementation is available online.

Key encapsulation mechanism (KEM) is an often used primitive in communication, closely related to public key encryption (PKE). Dual-receiver encryption (DRE) is another primitive closely related to PKE that allows a sender to encrypt a message to two different receivers. Most applications of DRE need the soundness property which guarantees that both receivers decrypt any ciphertext to the same message. Addition ally, IND-CPA security is often not enough and therefore schemes should satisfy a stronger notion like IND-CCA2. Meanwhile, an alternative to IND-CCA2 for KEMs is the IND-SB-CPA security notion which was proven to be strong enough to realize secure channels while in theory enabling the construction of more efficient schemes. Most IND-CCA2 security proofs rely on the FO transformation, which is only secure in the ROM, and the standard model DREs and KEMs are far from efficient. We fill this gap by providing a sound DRE and a KEM satisfying IND-CCA2 and IND-SB-CPA security respectively. Both schemes are based on RLWE, proven secure in the standard model, and have key sizes of 150 KB and ciphertext sizes of 100 KB, improving upon previous results by a factor of 10x to 100x.

In this work, we present new cryptanalytic results on the Ballet block cipher family, a simplified Lay-Massey ARX construction with a linear key schedule, winner of the symmetric algorithm category in the 2018–2020 Chinese National Cryptographic Algorithm Competition. Despite winning the competition, the cipher has received limited attention outside the Chinese Association for Cryptologic Research (CACR) community. We provide the first classical key recovery attacks in the literature, new explicit differential and linear trails (up to 15 rounds for differential, and 16 for linear, while the original paper only provided a bound for 9 rounds), improved impossible differential trails (8 rounds instead of 7), and the first differential-linear analysis of Ballet (up to 20 rounds). Our results lead to key recovery attacks on up to 16 rounds of Ballet-128/128/46 and 17 rounds of Ballet-128/256/48, thereby extending the cryptanalytic understanding of this ARX-based design and contributing new insight into its security margin, an area that the designers themselves note warrants further study.

Charles, Goren and Lauter proved that the supersingular $\ell$-isogeny graph is a Ramanujan graph, which is an optimal expander. Jordan and Zaytman argued that this is no longer true in dimension two, but Florit and Smith showed that those graphs exhibit good expansion properties nonetheless. Castryck, Decru and Smith however have pointed out that the higher-dimensional analogue setting should only consider a subset of all edges, namely the paths corresponding to $(\ell^k,\ell^k)$-isogenies, so-called good extensions, instead of all $(\ell^a,\ell^b,\ell^c,\ell^d)$-isogenies in general, which contain bad extensions too. Such bad extensions lead to many small cycles in the graph, which are a cryptographic problem due to collisions and a graph-theoretic nuisance as these superfluous edges counteract part of the expansion properties. Restricting to good extensions makes the resulting graph directed, as outgoing edges now depend on the incoming edge. We study $(\ell,\ell)$-level surfaces and $(\ell)^g$-isogeny digraphs restricted to good extensions for concrete small dimensions and degrees $\ell$. These graphs exhibit excellent expander properties: by our heuristic evidence, they are Ramanujan graphs for all primes $\ell$ in dimension 1, and for $\ell = 2$ in dimension 2. Our main conjecture implies that this would still be the case for $\ell=3$ in dimension 2, but not for any larger $\ell$ in dimension 2, or any $\ell$ in dimension 3 and up. Furthermore, we generalize the work of Florit and Smith from $\ell = 2$ to general primes $\ell$, by classifying all abelian surfaces with nontrivial automorphism groups and their actions on their maximal isotropic $(\ell,\ell)-$subgroups.

FAEST is a post-quantum digital signature candidate whose performance is dominated by repeated AES-CTR-based PRG calls in the VOLE-in-the-Head phase, yet its reference implementation provides no FAEST-specialized ARM NEON acceleration path. We present an ARM-oriented optimization that accelerates this bottleneck using general-purpose NEON SIMD instructions without relying on ARMv8 Crypto Extensions. The proposed implementation combines a register-resident 256-byte S-box with TBL/TBX-based four-stage SubBytes, 4-way and 8-way parallel AES block processing, a fixed-size PRG path specialized for the FAEST tree structure, and pthread-based batch-level parallelization of independent VOLE tasks. Evaluated on all 12 parameter sets of FAEST v2 on Raspberry Pi 4 and Apple M2, the combined optimization achieves speedups of up to $136.9\times$ and $330.1\times$, respectively, over the pure-C reference. On RPi4, the single-thread NEON implementation outperforms OpenSSL's software AES, and on M2, the full NEON-plus-pthread configuration outperforms the best available reference configuration, including hardware-accelerated OpenSSL, across all tested parameters.

Privacy-preserving smart contracts (PPSCs) extend blockchain computation from transparent execution to confidential applications, enabling mutually distrustful parties to jointly compute contract logic on private inputs. Existing PPSC designs can be categorized into two main paradigms: trusted hardware–based systems and cryptographic systems. Trusted hardware-based systems provide general programmability and the performance is usually close to non-confidential computation, but the hardware has to be trusted. In contrast, cryptographic systems require much lower trust on the hardware but the performance is usually much lower.

In this paper, we propose a hybrid PPSC framework that combines trusted hardware with cryptographic techniques, achieving both general programmability and reduced reliance on trusted hardware. Specifically, the TEE executes the smart contracts, but needs to authenticate the computation. A proof of the encrypted computational results is sent on-chain, and the blockchain authenticates the computational and aggregates the computational results using cryptographic approaches such as homomorphic encryption. In this way, the confidential smart contract via TEE is both efficient and general programmable, without being trusted. Meanwhile, the on-chain cryptographic approach does not introduce high overhead as it only authenticates and aggregates the results. We formalize the system model and security goals, and prove the correctness using the Universal Composability framework. Our implementation and evaluation on Intel SGX as the trusted hardware and Solidity as the smart contract show that our approach achieves nearly no degradation on the performance compared to non-confidential computation.

The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution. As these agents transition from isolated operation to collaborative ecosystems, we witness the emergence of the Agent-to-Agent (A2A) network, a paradigm where heterogeneous agents autonomously coordinate to solve multi-step tasks. While these networks may offer better task performance compared to simply using one agent to complete the entire task, they introduce systemic vulnerabilities, such as adversarial composition, semantic misalignment, and cascading operational failures, that existing agent alignment techniques cannot address. In this vision paper, we argue that the trustworthiness of A2A networks cannot be fully guaranteed via retrofitting on existing protocols that are largely designed for individual agents. Rather, it must be architected from the very beginning of the A2A coordination framework. We present a comprehensive conceptual framework that situates trust in A2A systems through four design pillars.

Graphs $D(n, q)$ and their connected components $CD(n, q)$ were defined 30 years ago. We observe shortly their applications to Extremal Graph Theory, Spectral Graph Theory, Algebraic Graph Theory, Symmetric Cryptography and Theory of Low Density Parity Check Codes. We introduce several new algorithms of Noncommutative Cryptography based on this graphs of large girth, In particular we propose modification of Diffie-Hellman protocol in terms of semigroup of walks of even length on the forest obtained as projective limit of $D(n, q)$ and the homomorphic image of this monoid, acting on the vector space $(F_q)^n$ as transformation group $G(n, q)$ of cubical polynomial transformation. The protocol allows users to elaborate collision vector from $(F_q)^n$ in time $O(n^2)$. The security of this schemes rests on the complexity of Conjugacy Power Problem for affine Cremona semigroup of automorphisms of $F_q[x_1, x_2, \dots, x_n]$. Inverse protocol of El Gamal type allows to use these scheme for encryption or creating of digital signature. Several obfuscations of these algorithm are given.

The assumed hardness of the Linear Code Equivalence problem (LCE) lies at the core of the security of the LESS signature scheme and other signature schemes with advanced functionalities. The LCE problem asks to determine whether two linear codes are equivalent. This equivalence is represented by a monomial matrix $Q$, i.e. the product of a diagonal matrix $D$ and a permutation matrix $P$. The recovery of $Q=DP$ is known to be reduced to the recovery of the permutation matrix $P$ alone. Exploiting this fact, we construct an algebraic model for LCE involving only the matrix $P$. To this end, we study the action of monomial matrices on linear codes using tools from algebraic geometry, including Plücker coordinates and fields of invariant rational functions. In particular, we analyse the action of diagonal matrices on linear codes, which can be interpreted as diagonal scaling of the coordinates of elements of the Grassmannian. We propose a method to determine algebraically independent generators of the field of rational functions invariant under this action, without relying on Reynolds operators or Gröbner basis computations. Furthermore, given two equivalent codes, we apply our results to explicitly construct, for each invariant function, a polynomial having $P$ as a root. However, the resulting polynomials are not of practical use: their degrees are high for cryptographically relevant parameters, and the number of monomials grows exponentially, making them infeasible to manipulate. Despite this limitation, our results are of theoretical interest, as they constitute the first application of these tools to the cryptanalysis of LCE and provide insight into how algebraic geometry and invariant theory can be employed in Cryptography.

Lookup Table (LUT) arguments are a central efficiency primitive in modern SNARKs, and existing high-performance constructions are largely tailored to large fields. Meanwhile, an increasingly important class of applications is natively ring-based, with arithmetic carried out over residue rings $\mathbb{Z}_Q:=\mathbb{Z}/Q\mathbb{Z}$. We find that naively extending field-based lookup table techniques to rings faces fundamental obstacles, which can lead to unsoundness, limited applicability, or poor efficiency.

We introduce $\mathsf{GlueLUT}$, a general framework for constructing LUT arguments over arbitrary residue ring $\mathbb{Z}_Q$ that supports arbitrary tables. Our main technical tool is a new primitive called Cross-Modulus Consistency (CMC) PIOP, proves that two witnesses defined over coprime moduli share the same underlying integer in the canonical range. Using our CMC PIOP as a glue, we perform the lookups over an auxiliary field $\mathbb{F}_P$ (for a prime $P>Q$) and then certify the consistency between the witness over $\mathbb{Z}_Q$ and the witness over $\mathbb{F}_P$, thereby avoiding the obstacles of constructing LUT arguments directly over rings. We further provide two optimized instantiations, $\mathsf{GlueLUT}$-$\mathsf{v1}$ for $Q=pq$ and $\mathsf{GlueLUT}$-$\mathsf{v2}$ for $Q=p^k$, capturing common modulus families in practice. Finally, we implement $\mathsf{GlueLUT}$-$\mathsf{v1}$ and $\mathsf{GlueLUT}$-$\mathsf{v2}$ as stand-alone PIOPs and report prototype results that corroborate our theoretical efficiency.

We explore the use of level structures to generalize the SQIsign signature scheme. We give a general framework where, given the public key and the commitment, the challenge is to exhibit an isogeny between them with an additional requirement, namely to map a chosen level structure to nother. We then instantiate the framework using 1-dimensional and 2-dimensional isogenies. In doing that we provide a new explicit Deuring correspondence for supersingular elliptic curves with level structures and solve new constrained norm equations.