International Association
for Cryptologic Research

This paper examines how quantum computing enhances the encryption system. It studies the relationship between cryptography and quantum physics. The paper considers the historical progression of encryption techniques paying attention to the altering nature of security challenges. Moreover, it analyzes the basic principles of quantum computing, describing its theoretical concept and its advantages over classical systems in terms of potential performance. Also, it focuses on an in-depth analysis of Grovers’ Algorithm showing its unique searching capability and its implications for encryption schemes. The paper also reviews the limitations of Grover’s Algorithm that could make it vulnerable to attacks and how to make it safer. Also, the methods of quantum computing that create strong encryption are briefly outlined. Overall, in the quest for secure systems of communication in the era of quantum, the paper aims at the futuristic paradigm shift by considering the emergence of Quantum-Powered Encryption Systems. It answers key questions about this subject through both, qualitative and quantitative analysis. The provided scientific report supplements the existing body of knowledge about the relationship between quantum computers and encryption systems and sets the foundation for better and more secure encryption for the digital world.

The security of the Elliptic Curve Digital Signature Algorithm (ECDSA) depends on the uniqueness and secrecy of the nonce, which is used in each signature. While it is well understood that nonce $k$ reuse across two distinct messages can leak the private key, we show that even if a distinct value is used for $k_2$, where an affine relationship exists in the form of: \(k_m = a \cdot k_n + b\), we can also recover the private key. Our method requires only two signatures (even over the same message) and relies purely on algebra, with no need for lattice reduction or brute-force search(if the relationship, or offset, is known). To our knowledge, this is the first closed-form derivation of the ECDSA private key from only two signatures over the same message, under a known affine relationship between nonces.

Proxy re-encryption (PRE) schemes allow a delegator to designate a proxy to re-encrypt its ciphertext into a ciphertext that the delegatee can decrypt. In contrast, the proxy gains nothing helpful from this transformation. This decryption-power transfer is proper in applications of encrypted email forwarding, key escrow, and publish/subscribe systems.

The security notions for PRE are inherited from the standard public key encryption (PKE) schemes, i.e., indistinguishability under chosen-plaintext attacks (CPA) and security under chosen-ciphertext attacks (CCA). A recently popular notion, indistinguishability under honest re-encryption attacks (HRA), was proposed by Cohen in 2019, indicating that CPA security is insufficient for PRE because some CPA-secure PRE leaks the secret key of the delegator. Many post-quantum secure PRE schemes have recently been designed under the HRA security model.

However, HRA security differs from traditional CCA security, and there is no known reduction between them. The existing results show they appear to be incompatible. This paper aims to bridge those two security notions via reductions. In addition, we found that many existing HRA-secure schemes are vulnerable to collusion. We provide a generic transformation from a CPA-secure PRE to a collusion-resistant and CPA-secure PRE. This transformation also applies to HRA-secure PREs.

Federated Learning (FL) allows clients to engage in learning without revealing their raw data. However, traditional FL focuses on developing a single global model for all clients, limiting their ability to have personalized models tailored to their specific needs. Personalized FL (PFL) enables clients to obtain their customized models, either with or without a central party. Current PFL research includes mechanisms to detect poisoning attacks, in which a couple of malicious nodes try to manipulate training convergence by submitting misleading data. However, these detection approaches often overlook privacy concerns, as they require clients to share their models with all other clients. This paper extends BALANCE, a personalized poisoning detection mechanism based on client models and their expectations. Our method enhances both security and privacy by ensuring clients are not required to share their model data with other clients. By leveraging server-assisted PFL and Fully Homomorphic Encryption (FHE), we enable a central party to identify unpoisoned clients from the perspective of individual clients and train personalized models securely. Additionally, we introduce an efficient personalized client selection algorithm that prevents redundant checks and ensures the inheritance of unpoisoned clients.

We present concrete techniques for adapting the protocols of Mohassel et al (CCS 2020) and Badrinarayanan et al (CCS 2022) for compute SQL-like querying operations on secret shared database tables to the two party setting. The afore mentioned protocols are presented in a generic setting with access to certain idealized functionalities, e.g. secret shared permutations. However, they only instantiate their protocols in the honest majority three party setting due to other settings being considered too inefficient. We show that this is no longer the case. In particular, the recent work of Peceny et al. (eprint 2024) gives a concretely efficient two party permutation protocol. Additionally, we give a new and highly efficient protocol for evaluating the strong PRF recently proposed by Alamati et al. (Crypto 2024). Building on these advancements, along with a variety of protocol improvements and significant cryptographic engineering, our open source implementation demonstrate concretely efficient two party SQL-like querying functionality on secret shared data.

We focus on the two party setting with secret shared input and output tables. The first protocol $\Pi_\textsf{Join-OO}$ is designed for the setting where the join keys are unique, similar to Private Set Intersection (PSI) except that the inputs and output are secret shared. This protocol is constant round and $O(n)$ running time. The secret protocol $\Pi_\textsf{Join-OM}$ allows one of the tables to contain repeating join keys. Our instantiations achieves $O(n\log n)$ running time and $O(\log n)$ rounds of interaction.

Searchable encryption (SE) enables privacy-preserving keyword search on encrypted data. Public-key SE (PKSE) supports multi-user searches but suffers from high search latency due to expensive public-key operations. Symmetric SE (SSE) offers a sublinear search but is mainly limited to single-user settings. Recently, hybrid SE (HSE) has combined SSE and PKSE to achieve the best of both worlds, including multi-writer encrypted search functionalities, forward privacy, and sublinear search with respect to database size. Despite its advantages, HSE inherits critical security limitations, such as susceptibility to dictionary attacks, and still incurs significant overhead for search access control verification, requiring costly public-key operation invocations (i.e., pairing) across all authorized keywords. Additionally, its search access control component must be rebuilt periodically for forward privacy, imposing substantial writer overhead. In this paper, we propose Hermes, a new HSE scheme that addresses the aforementioned security issues in prior HSE designs while maintaining minimal search complexity and user efficiency at the same time. Hermes enables multi-writer encrypted search functionalities and offers forward privacy along with resilience to dictionary attacks. To achieve this, we develop a new identity-based encryption scheme with hidden identity and key-aggregate properties, which could be of independent interest. We also design novel partitioning and epoch encoding techniques in Hermes to minimize search complexity and offer low user overhead in maintaining forward privacy. We conducted intensive experiments to assess and compare the performance of Hermes and its counterpart on commodity hardware. Experimental results showed that Hermes performs search one to two orders of magnitude faster than the state-of-the-art HSE while offering stronger security guarantees to prevent dictionary and injection attacks.

The major Fully Homomorphic Encryption (FHE) schemes guarantee the privacy of the encrypted message only in the honest-but-curious setting, when the server follows the protocol without deviating. However, various attacks in the literature show that an actively malicious server can recover sensitive information by executing incorrect functions, tampering with ciphertexts, or observing the client’s reaction during decryption.

Existing integrity solutions for FHE schemes either fail to guarantee circuit privacy, exposing the server's computations to the client, or introduce significant computational overhead on the prover by requiring proofs of FHE operations on ciphertexts.

In this work, we present Fherret, a novel scheme leveraging the MPC-in-the-Head (MPCitH) paradigm to provide a proof of correct-and-honest homomorphic evaluation while preserving circuit privacy. This proof guarantees that the client can safely decrypt the ciphertext obtained from the server without being susceptible to reaction-based attacks, such as verification and decryption oracle attacks. Additionally, this proof guarantees that the server’s evaluation maintains correctness, thereby protecting the client from $\mathsf{IND}\text{-}\mathsf{CPA}^{\mathsf{D}}$-style attacks.

Our solution achieves a prover overhead of $4\lambda$ homomorphic evaluations of random functions from the function space $\mathcal{F}$, while retaining a competitive verifier overhead of $2 \lambda$ homomorphic evaluations and a communication size proportional to $\sqrt{2\lambda}$ times the size of a function from $\mathcal{F}$.

Furthermore, Fherret is inherently parallelizable, achieving a parallel computation overhead similar to a homomorphic evaluation of a random function from $\mathcal{F}$ for both the prover and the verifier.

This document is a preliminary version of what is intended to be submitted to NIST by Zama as part of their threshold call. The document also serves as partial documentation of the protocols used in the Zama MPC system for threshold TFHE.

However, note that the Zama software includes many optimizations built on top of the simple specifications given here. In particular the TFHE parameters given here are larger than those used by the Zama software. This is because the Zama TFHE library contains optimizations which are beyond the scope of this document. Thus the parameters given in this document are compatible with the description of TFHE given here, and take no account of the extra optimizations in the Zama software.

Also note that we describe more protocols than that provided in the Zama software. In particular this document describes BGV and BFV threshold implementations, MPC-in-the-Head based proofs of correct encryption.

We present mechanisms to perform robust threshold key generation and decryption for Fully Homomorphic Encryption schemes such as BGV, BFV and TFHE, in the case of super honest majority, t < n/3, or t < n/4, in the presence of malicious adversaries.

The main mechanism for threshold decryptions follow the noise flooding principle, which we argue is sufficient for BGV and BFV. For TFHE a more subtle technique is needed to apply noise flooding, since TFHE parameters are small. To deal with all three FHE scheme, and obtain a unified framework for all such schemes, we are led to consider secret sharing over Galois Rings and not just finite fields.

We consider two sets of threshold profiles, depending on whether binomial(n,t) is big or small. In the small case we obtain for all schemes an asynchronous protocol for robust threshold decryption, and we obtain a robust synchronous protocol for threshold key generation; both with t < n/3. For the large case we only support TFHE, and our protocols require an “offline phase” which requires synchronous networks and can “only” tolerate t < n/4.

The threshold key generation operation, and the above mentioned offline phase, require access to a generic offline MPC functionality over arbitrary Galois Rings. This functionality is fully specified here. Finally, we present Zero-Knowledge proof techniques for proving the valid encryption of an FHE ciphertext. These proofs are important in a number of application contexts.

Deep learning-based side-channel analysis is an extremely powerful option for profiling side-channel attacks. However, to perform well, one needs to select the neural network model and training time hyperparameters carefully. While many works investigated these aspects, random search could still be considered the current state-of-the-art. Unfortunately, random search has drawbacks, since the chances of finding a good architecture significantly drop when considering more complex targets. In this paper, we propose a novel neural architecture search approach for SCA based on grammatical evolution - SCAGE. We define a custom SCA grammar that allows us to find well-performing and potentially unconventional architectures. We conduct experiments on four datasets, considering both synchronized and desynchronized versions, as well as using feature intervals or raw traces. Our results show SCAGE to perform extremely well in all settings, outperforming random search and related works in most of the considered scenarios.

At CRYPTO 2019, Gohr pioneered the integration of differential cryptanalysis with neural networks, demonstrating significant advantages over traditional distinguishers. Subsequently, at Inscrypt 2020, Su et al. proposed the concept of constructing polyhedral differential neural distinguishers by leveraging multiple effective input differences. More recently, at FSE 2024, Bellini et al. introduced a general-purpose tool for automating the training of single-key differential neural distinguishers for various block ciphers. Inspired by this body of work, we aim to extend automated search techniques to related-key differential neural distinguishers, enabling the discovery of effective input differences and key differences for such distinguishers. To achieve this, we employ a genetic optimization algorithm to identify effective differential combinations. To validate the efficacy of our method, we apply it to the Simeck and Simon cipher families, successfully identifying effective differential combinations for the three variants of Simeck and ten variants of Simon. Furthermore, inspired by the concept of polyhedral neural distinguishers, we adopt a novel data format that leverages multiple distinct input differences and key differences to construct positive and negative samples, providing the neural network with a richer set of features. Our approach not only identify high-quality distinguishers for previously unexplored cipher variants but also achieve higher accuracy for related-key differential neural distinguishers compared to the state-of-the-art.

Amortized bootstrapping techniques have been proposed for FHEW/TFHE to efficiently refresh multiple ciphertexts simultaneously within a polynomial modulus. Although recent proposals have very efficient asymptotic complexity, reducing the amortized cost essentially to $\tilde{O}(1)$ FHE multiplications, the practicality of such algorithms still suffers from substantial overhead and high decryption failure rates (DFR). In this study, we improve upon one of the state-of-the-art amortized bootstrapping algorithms (Guimarães et al., ASIACRYPT 2023) for FHEW/TFHE-like schemes by introducing an alternative algorithmic strategy. Specifically, we combine Guimarães et al.'s strategy based on a two-part NTT with an incomplete Number Theoretic Transform (NTT) algorithm. As a result, we demonstrate a 2.12$\times$ speedup compared to the algorithm of Guimarães et al. and a $1.12\times$ improvement over the state-of-the-art (sequential) TFHE-rs while achieving a DFR close to $2^{-32}$ for 7-bit messages, although the DFR is higher for 8-bit messages. We also explore trade-offs between execution time and DFR, identifying parameter sets that improve execution time of Guimarães et al. by $1.41\times$, while simultaneously reducing the DFR by a factor of $2^{-22}$ for 8-bit messages.

PLONK is a prominent universal and updatable zk-SNARK for general circuit satisfiability, which allows a prover to produce a short certificate of the validity of a certain statement/computation. Its expressive model of computation and its highly efficient verifier complexity make PLONK a powerful tool for a wide range of blockchain applications.

Supporting standard cryptographic primitives (such us ECDSA over SECP256k1) or advanced recursive predicates (e.g. incrementally verifiable computation) on a SNARK presents a significant challenge. It requires so-called foreign-field arithmetic (enforcing constraints over algebraic fields that differ from the SNARK native field) which was previously believed to incur an overhead of two or three orders of magnitude.

We build on the techniques by Lubarov and Baylina and observe that, by considering tight bounds on their encoding of foreign-field multiplication, the number of PLONK constraints can be significantly reduced. We show that these techniques also extend to elliptic curve emulation, with an overhead of just one order of magnitude (with respect to its native counterpart). We validate soundness and completeness of our main results in EasyCrypt. Finally, we implement an open-source library with support for foreign-field arithmetic. Our experimental results showcase the generality of our techniques and confirm their suitability for real-world applications.

In an anonymous credential system, users collect credentials from issuers, and can use their credentials to generate privacy-preserving identity proofs that can be shown to third-party verifiers. Since the introduction of anonymous credentials by Chaum in 1985, there has been promising advances with respect to system design, security analysis and real-world implementations of anonymous credential systems.

In this paper, we examine Hyperledger AnonCreds, an anonymous credential system that was introduced in 2017 and is currently undergoing specification. Despite being implemented in deployment-ready identity system platforms, there is no formal security analysis of the Hyperledger AnonCreds protocol. We rectify this, presenting syntax and a security model for, and a first security analysis of, the Hyperledger AnonCreds protocol. In particular, we demonstrate that Hyperledger AnonCreds is correct, and satisfies notions of unforgeability and anonymity. We conclude with a discussion on the implications of our findings, highlighting the importance of rigorous specification efforts to support security evaluation of real-world cryptographic protocols.

Safety and liveness are the two classical security properties of consensus protocols. Recent works have strengthened safety with accountability: should any safety violation occur, a sizable fraction of adversary nodes can be proven to be protocol violators. This paper studies to what extent analogous accountability guarantees are achievable for liveness. To reveal the full complexity of this question, we introduce an interpolation between the classical synchronous and partially-synchronous models that we call the $x$-partially-synchronous network model in which, intuitively, at most an $x$ fraction of the time steps in any sufficiently long interval are asynchronous (and, as with a partially-synchronous network, all time steps are synchronous following the passage of an unknown "global stablization time"). We prove a precise characterization of the parameter regime in which accountable liveness is achievable: if and only if $x < 1/2$ and $f < n/2$, where $n$ denotes the number of nodes and $f$ the number of nodes controlled by an adversary. We further refine the problem statement and our analysis by parameterizing by the number of violating nodes identified following a liveness violation, and provide evidence that the guarantees achieved by our protocol are near-optimal (as a function of $x$ and $f$). Our results provide rigorous foundations for liveness-accountability heuristics such as the "inactivity leaks" employed in Ethereum.

An interactive aggregate signature scheme allows $n$ signers, each with their own secret/public key pair $(sk_i, pk_i)$ and message $m_i$, to jointly produce a short signature that simultaneously witnesses that $m_i$ has been signed under $pk_i$ for every $i \in \{1, \dots, n\}$. Despite the large potential for savings in terms of space and verification time, which constitute the two main bottlenecks for large blockchain systems such as Bitcoin, aggregate signatures have received much less attention than the other members of the multi-party signature family, namely multi-signatures such as $\mathsf{MuSig2}$ and threshold signatures such as $\mathsf{FROST}$.

In this paper, we propose $\mathsf{DahLIAS}$, the first aggregate signature scheme with constant-size signatures—a signature has the same shape as a standard Schnorr signature—directly based on discrete logarithms in pairing-free groups. The signing protocol of $\mathsf{DahLIAS}$ consists of two rounds, the first of which can be preprocessed without the message, and verification (for a signature created by $n$ signers) is dominated by one multi-exponentiation of size $n+1$, which is asymptotically twice as fast as batch verification of $n$ individual Schnorr signatures.

$\mathsf{DahLIAS}$ is designed with real-world applications in mind. Besides the aforementioned benefits of space savings and verification speedups, $\mathsf{DahLIAS}$ offers key tweaking, a technique commonly used in Bitcoin to derive keys in hierarchical deterministic wallets and to save space as well as enhance privacy on the blockchain. We prove $\mathsf{DahLIAS}$ secure in the concurrent setting with key tweaking under the (algebraic) one-more discrete logarithm assumption in the random oracle model.

Constructing and implementing isogeny-based cryptographic primitives is an active research. In particular, performing length-$n$ isogenies walks over quadratic field extensions of $\mathbb{F}_p$ plays an exciting role in some constructions, including Hash functions, Verifiable Delay Functions, Key-Encapsulation Mechanisms, and generic proof systems for isogeny knowledge. Remarkably, many isogeny-based constructions, for efficiency, perform $2$-isogenies through square root calculations.

This work analyzes the idea of using $3$-isogenies instead of $2$-isogenies, which replaces the requirement of calculating square roots with cube roots. Performing length-$m$ $3$-isogenies allows shorter isogeny walks than when employing length-$n$ $2$-isogenies since a cube root calculation costs essentially the same as computing a square root, and we require $3^m \approx 2^n$ to provide the same security level.

We propose an efficient mapping from arbitrary supersingular Montgomery curves defined over $\mathbb{F}_{p^2}$ to the $3$-isogeny curve model from Castryck, Decru, and Vercauteren (Asiacrypt 2020); a deterministic algorithm to compute all order-$3$ points on arbitrary supersingular Montgomery curves, and an efficient algorithm to compute length-$m$ $3$-isogeny chains.

We improve the length-$m$ $3$-isogeny walks required by the KEM from Nakagawa and Onuki (CRYPTO 2024) by using our results and introducing more suitable parameter sets that are friendly with C-code implementations. In particular, our experiments illustrate an improvement of 26.41\%--35.60\% in savings when calculating length-$m$ $3$-isogeny chains and using our proposed parameters instead of those proposed by Nakagawa and Onuki (CRYPTO 2024).

Finally, we enhance the key generation of $\mathsf{CTIDH}$-2048 by including radical $3$-isogeny chains over the basefield $\mathbb{F}_p$, reducing the overhead of finding a $3$-torsion basis as required in some instantiations of the $\mathsf{CSIDH}$ protocol. Our experiments illustrate the advantage of radical $3$ isogenies in the key generation of $\mathsf{CTIDH}$-2048, with an improvement close to 4x faster than the original $\mathsf{dCTIDH}$.

The securities of a large fraction of zero-knowledge arguments of knowledge schemes rely on the discrete logarithm (DL) assumption or the discrete logarithm relation assumption, such as Bulletproofs (S&P 18) and compressed $\Sigma$-protocol (CRYPTO 20). At the heart of these protocols is an interactive proof of knowledge between a prover and a verifier showing that a Pedersen vector commitment $P=h^{\rho}\cdot\textbf{g}^{\textbf{x}}$ to a vector $\textbf{x}$ satisfies multi-variate equations, where the DL relations among the vector of generators $\textbf{g}$ are unknown. However, in some circumstances, the prover may know the DL relations among the generators, and the DL relation assumption no longer holds, such as ring signatures, ring confidential transactions (RingCT) and K-out-of-N proofs, which will make the soundness proof of these protocols infeasible. This paper is concerned with a problem called knowledge of known discrete logarithms (KKDL) that appears but has not been clearly delineated in the literature. Namely, it asks to prove a set of multi-exponent equalities, starting with the fact that the prover may know the DL relations among the generators of these equalities. Our contributions are three-fold: (1) We propose a special honest-verifier zero-knowledge protocol for the problem. Using the Fiat-Shamir heuristic and the improved inner-product argument of Bulletproofs, the proof size of our protocol is logarithmic to the dimension of the vector. (2) As applications, our protocol can be utilized to construct logarithmic-size RingCT securely which fixes the issues of Omniring (CCS 19), ring signatures (with signature size $2\cdot \lceil \log_2(N) \rceil+10$ for ring size $N$) and $K$-out-of-$N$ proof of knowledge (with proof size $2\cdot \lceil \log_2(N) \rceil+14$) which achieves the most succinct proof size improving on previous results. Meanwhile, we propose the first account-based multi-receiver privacy scheme considering the sender's privacy with logarithmic proof size (to the best of our knowledge). (3) We describe an attack on RingCT-3.0 (FC 20) where an attacker can spend a coin of an arbitrary amount that never existed on the blockchain.

In this work, we explore neural network design options for discriminating Random Number Generators(RNG), as a complement to existing statistical test suites, being a continuation of a recent paper of the aothors. Specifically, we consider variations in architecture and data preprocessing. We test their impact on the network's ability to discriminate sequences from a low-quality RNG versus a high-quality one—that is, to discriminate between "optimal" sequence sets and those from the generator under test. When the network fails to distinguish them, the test is passed. For this test to be useful, the network must have real discrimination capabilities. We review several network design possibilities showing significant differences in the obtained results. The best option presented here is convolutional networks working on 5120-byte sequences.

Side-channel analysis (SCA) has emerged as a critical field in securing hardware implementations against potential vulnerabilities. With the advent of artificial intelligence(AI), neural network-based approaches have proven to be among the most useful techniques for profiled SCA. Despite the success of NN-assisted SCA, a critical challenge remains, namely understanding predictive uncertainty. NNs are often uncertain in their predictions, leading to incorrect key guesses with high probabilities, corresponding to a higher rank associated with the correct key. This uncertainty stems from multiple factors, including measurement errors, randomness in physical quantities, and variability in NN training. Understanding whether this uncertainty arises from inherent data characteristics or can be mitigated through better training is crucial. Additionally, if data uncertainty dominates, identifying specific trace features responsible for misclassification becomes essential.

We propose a novel approach to estimating uncertainty in NN-based SCA by leveraging Renyi entropy, which offers a generalized framework for capturing various forms of uncertainty. This metric allows us to quantify uncertainty in NN predictions and explain its impact on key recovery. We decompose uncertainty into epistemic (model-related) and aleatoric (data-related) components. Given the challenge of estimating probability distributions in high-dimensional spaces, we use matrix-based Renyi α-entropy and α-divergence to better approximate leakage distributions, addressing the limitations of KL divergence in SCA. We also explore the sources of uncertainty, e.g., resynchronization, randomized keys, as well as hyperparameters related to NN training. To identify which time instances (features in traces) contribute most to uncertainty, we also integrate SHAP explanations with our framework, overcoming the limitations of conventional sensitivity analysis. Lastly, we show that predictive uncertainty strongly correlates with standard SCA metrics like rank, offering a complementary measure for evaluating attack complexity. Our theoretical findings are backed by extensive experiments on available datasets and NN models.

As billions of people rely on end-to-end encrypted messaging, the exposure of metadata, such as communication timing and participant relationships, continues to deanonymize users. Asynchronous metadata-hiding solutions with strong cryptographic guarantees have historically been bottlenecked by quadratic $O(N^2)$ server computation in the number of users $N$ due to reliance on private information retrieval (PIR). We present Myco, a metadata-private messaging system that preserves strong cryptographic guarantees while achieving $O(N \log^2 N)$ efficiency. To achieve this, we depart from PIR and instead introduce an oblivious data structure through which senders and receivers privately communicate. To unlink reads and writes, we instantiate Myco in an asymmetric two-server distributed-trust model where clients write messages to one server tasked with obliviously transmitting these messages to another server, from which clients read. Myco achieves throughput improvements of up to 302x over multi-server and 2,219x over single-server state-of-the-art systems based on PIR.