International Association
for Cryptologic Research

Key derivation functions (KDFs) are integral to many cryptographic protocols. Their functionality is to turn raw key material, such as a Diffie-Hellman secret, into a strong cryptographic key that is indistinguishable from random. This guarantee was formalized by Krawczyk together with the seminal introduction of HKDF (CRYPTO 2010), in a model where the KDF only takes a single key material input. Modern protocol designs, however, regularly need to combine multiple secrets, possibly even from different sources, with the guarantee that the derived key is secure as long as at least one of the inputs is good. This is particularly relevant in settings like hybrid key exchange for quantum-safe migration. Krawczyk's KDF formalism does not capture this goal, and there has been surprisingly little work on the security considerations for KDFs since then.

In this work, we thus revisit the syntax and security model for KDFs to treat multiple, possibly correlated inputs. Our syntax is assertive: We do away with salts, which are needed in theory to extract from arbitrary sources in the standard model, but in practice, they are almost never used (or even available) and sometimes even misused, as we argue. We use our new model to analyze real-world multi-input KDFs—in Signal's X3DH protocol, ETSI's TS 103 744 standard, and MLS' combiner for pre-shared keys—as well as new constructions we introduce for specialized settings—e.g., a purely blockcipher-based one. We further discuss the importance of collision resistance for KDFs and finally apply our multi-input KDF model to show how hybrid KEM key exchange can be analyzed from a KDF perspective.

Proxy re-encryption (PRE) schemes enable a semi-honest proxy to transform a ciphertext of one user $i$ to another user $j$ while preserving the privacy of the underlying message. Multi-hop PRE schemes allow a legal ciphertext to undergo multiple transformations, but for lattice-based multi-hop PREs, the number of transformations is typically bounded due to the increase of error terms. Recently, Zhao et al. (Esorics 2024) introduced a lattice-based unbounded multi-hop (homomorphic) PRE scheme that supports an unbounded number of hops. Nevertheless, their scheme only achieves the selective CPA security. In contrast, Fuchsbauer et al. (PKC 2019) proposed a generic framework for constructing HRA-secure unbounded multi-hop PRE schemes from FHE. Despite this, when instantiated with state-of-the-art FHEW-like schemes, the overall key size and efficiency remain unsatisfactory.

In this paper, we present a lattice-based unbounded multi-hop PRE scheme with the stronger adaptive HRA security (i.e. security against honest re-encryption attacks), which is more suitable for practical applications. Our scheme features an optimized re-encryption process based on the FHEW-like blind rotation, which resolves the incompatibility between the noise flooding technique and Fuchsbauer et al. 's framework when instantiated with FHEW-like schemes. This results in reduced storage requirements for public keys and offers higher efficiency. Moreover, our optimized unbounded multi-hop PRE scheme can be modified to an unbounded homomorphic PRE, a scheme allowing for arbitrary homomorphic computations over fresh, re-encrypted, and evaluated ciphertexts.

This paper revisits the Hamming Weight (HW) labelling function for machine learning assisted side channel attacks. Contrary to what has been suggested by previous works, our investigation shows that, when paired with modern deep learning architectures, appropriate pre-processing and normalization techniques; it can perform as well as the popular identity labelling functions and sometimes even beat it. In fact, we hereby introduce a new machine learning method, dubbed, that helps solve the class imbalance problem associated to HW, while significantly improving the performance of unprofiled attacks. We additionally release our new, easy to use python package that we used in our experiments, implementing a broad variety of machine learning driven side channel attacks as open source, along with a new dataset AES_nRF, acquired on the nRF52840 SoC.

The ECDSA (Elliptic Curve Digital Signature Algorithm) is used in many blockchain networks for digital signatures. This includes the Bitcoin and the Ethereum blockchains. While it has good performance levels and as strong current security, it should be handled with care. This care typically relates to the usage of the nonce value which is used to create the signature. This paper outlines the methods that can be used to break ECDSA signatures, including revealed nonces, weak nonce choice, nonce reuse, two keys and shared nonces, and fault attack.

The increased popularity of large language models (LLMs) raises serious privacy concerns, where users' private queries are sent to untrusted servers. Many cryptographic techniques have been proposed to provide privacy, such as secure multiparty computation (MPC), which enables the evaluation of LLMs directly on private data. However, cryptographic techniques have been deemed impractical as they introduce large communication and computation. On the other hand, many obfuscation techniques have been proposed, such as split inference, where part of the model is evaluated on edge devices to hide the input data from untrusted servers, but these methods provide limited privacy guarantees.

We propose Fission, a privacy-preserving framework that improves latency while providing strong privacy guarantees. Fission utilizes an MPC network for linear computations, while nonlinearities are computed on a separate evaluator network that receives shuffled values in the clear and returns nonlinear functions evaluated at these values back to the MPC network. As a result, each evaluator only gets access to parts of the shuffled data, while the model weights remain private. We evaluate fission on a wide set of LLMs and compare it against prior works. Fission results in up to eight times faster inference and eight times reduced bandwidth compared to prior works while retaining high accuracy. Finally, we construct an attack on obfuscation techniques from related works that show significant information leakage, and we demonstrate how Fission enhances privacy.

As real-world networks such as social networks and computer networks are often complex and distributed, modeling them as multilayer graphs is gaining popularity. For instance, when studying social interactions across platforms like LinkedIn, Facebook, TikTok, and Bluesky, users may be connected on several of these platforms. To identify important nodes/users, the platforms might wish to analyze user interactions using, e.g., centrality measures when accounting for connections across all platforms. That raises the challenge for platforms to perform such computation while simultaneously protecting their user data to shelter their own business as well as uphold data protection laws. This necessitates designing solutions that allow for performing secure computation on a multilayer graph which is distributed among mutually distrusting parties while keeping each party's data hidden.

The work of Asharov et al. (WWW'17) addresses this problem by designing secure solutions for centrality measures that involve computing the truncated Katz score and reach score on multilayer graphs. However, we identify several limitations in that work which render the solution inefficient or even unfeasible for realistic networks with significantly more than 10k nodes. We address these limitations by designing secure solutions that are significantly more efficient and scalable. In more detail, given that real-world graphs are known to be sparse, our solutions move away from an expensive matrix-based representation to a more efficient list-based representation. We design novel, secure, and efficient solutions for computing centrality measures and prove their correctness. Our solutions drastically reduce the asymptotic complexity from the prohibitive $\mathcal{O}(|\mathsf{V}|^2)$ even for the fastest solution by Asharov et al. down to $\mathcal{O}(|\mathsf{V}|\log |\mathsf{V}|)$, for $|\mathsf{V}|$ nodes. To design our solutions, we extend upon the secure graph computation framework of Koti et al. (CCS'24), providing a novel framework with improved capabilities in multiple directions. Finally, we provide an end-to-end implementation of our secure graph analysis framework and establish concrete efficiency improvements over prior work, observing several orders of magnitude improvement.

We introduce a new bootstrapping equation for the CKKS homomorphic encryption scheme of approximate numbers. The original bootstrapping approach for CKKS consists in homomorphically evaluating a polynomial that approximates the modular reduction modulo q. In contrast, our new bootstrapping equation directly embeds the additive group modulo q into the complex roots of unity, which can be evaluated natively in the CKKS scheme. Due to its reduced multiplicative depth, our new bootstrapping equation achieves a 7x latency improvement for a single slot compared to the original CKKS bootstrapping, though it scales less efficiently when applied to a larger number of slots.

Recently, Dupin and Abelard proposed a broadcast encryption scheme which outperforms the Complete Subtree-based and Subset Difference broadcast encryption in terms of encryption cost and bandwidth requirement. However, Dupin and Abelard acknowledge that the worst-case bound for bandwidth requirement of Complete Subtree approach can be reached in their scheme as well. In this paper, we answer the call to further reduce this bandwidth bottleneck. We first provide concrete analysis to show how this worst-case upper-bound is reached from concrete Boolean functions. Then we present two improved broadcast encryption schemes to significantly reduce this worst-case bandwidth consumption for further optimization of Dupin and Abelard’s technique. Our proposed approach ADC-BE, composed of two algorithms, AD-BE and AC-BE, can significantly optimize this worst-case complexity from n/2 down to 1 for a system of n users. This is efficient especially for large number of users in the system. Our proposed schemes combines the algebraic normal form, disjunctive normal form, and conjunctive normal form to optimize a Boolean function to its minimized representation. In addition, our approaches can be made secure against quantum adversaries and are therefore post-quantum, where both algorithms AD-BE and AC-BE require minimal assumptions based on existence of one-way function.

Asynchronous Complete Secret Sharing (ACSS) is a foundational module for asynchronous networks, playing a critical role in cryptography. It is essential for Asynchronous Secure Multi-Party Computation (AMPC) and, with termination, is widely applied in Validated Asynchronous Byzantine Agreement (VABA) and Asynchronous Distributed Key Generation (ADKG) to support secure distributed systems. Currently, there are relatively few statistical secure ACSS protocols that can guarantee termination, and their communication complexity is relatively high. To reduce communication complexity, we propose a new multi-receiver signature scheme, ARICP, which supports linear operations on signatures. Leveraging the ARICP scheme and the properties of symmetric polynomials, we propose an ACSS protocol that ensures termination and optimal resilience ($t < n / 3$) with $\mathcal{O}(n^{2}\kappa$) bits per sharing. Compared with the best-known result of ACSS protocols that guarantee termination [CP23], the amortized communication complexity of our protocol is reduced by a factor of $\mathcal{O}(n)$.

In Hamming Quasi-Cyclic (HQC), one of the finalists in the NIST competition for the standardization of post-quantum cryptography, decryption relies on decoding a noisy codeword through a public error-correcting code. The noise vector has a special form that depends on the secret key (a pair of sparse polynomials). However, the decoder, which is currently employed in HQC, is agnostic to the secret key, operating under the assumption that the error arises from a Binary Symmetric Channel (BSC). In this paper, we demonstrate that this special noise structure can instead be leveraged to develop more powerful decoding strategies.

We first study the problem from a coding-theoretic perspective. The current code design, which admits a non-zero decryption failure rate, is close to optimal in the setting of a decoder that is agnostic to the error structure. We show that there are code-decoder pairs with a considerably shorter code length that can guarantee unique decoding by taking the error structure into account. This result is non-constructive, i.e., we do not provide an explicit code construction and it remains open whether efficient decoding is possible. Nevertheless, it highlights the potential that can be tapped by taking the error structure into account. We then argue that, in practice, the matter of decoding in HQC can be related to solving an instance of the noisy syndrome decoding problem, in which the parity-check matrix is constituted by the polynomials in the secret key. We show that, using decoders for Low-Density Parity-Check (LDPC) and Moderate-Density Parity-Check (MDPC) codes, one can significantly reduce the entity of the noise and, de facto, also the Decoding Failure Rate (DFR) of the HQC decoder.

This preliminary study leaves some open questions and problems. While it shows that decoding in HQC can be improved, the modeling of the DFR gets more complicated: even for the basic decoder we propose in this paper, we have not been able to devise a reliable DFR model. This is likely due to the fact that the decoder structure resembles the iterative nature of LDPC/MDPC decoders, for which devising a reliable DFR estimation is a well-known difficult problem.

Electronic voting schemes typically ensure ballot privacy by assuming that the decryption key is distributed among tallying authorities, preventing any single authority from decrypting a voter’s ballot. However, this assumption may fail in a fully dishonest environment where all tallying authorities collude to break ballot privacy. In this work, we introduce the notion of anamorphic voting, which enables voters to convey their true voting intention to an auditor while casting an (apparently) regular ballot. We present new cryptographic techniques demonstrating that several existing voting schemes can support anamorphic voting.

Private information retrieval (PIR) allows to privately read a chosen bit from an $N$-bit database $x$ with $o(N)$ bits of communication. Lin, Mook, and Wichs (STOC 2023) showed that by preprocessing $x$ into an encoded database $\hat x$, it suffices to access only $polylog(N)$ bits of $\hat x$ per query. This requires $|\hat x|\ge N\cdot polylog(N)$, and prohibitively large server circuit size.

We consider an alternative preprocessing model (Boyle et al. and Canetti et al., TCC 2017), where the encoding $\hat x$ depends on a client's short secret key. In this secret-key PIR (sk-PIR) model we construct a protocol with $O(N^\epsilon)$ communication, for any constant $\epsilon>0$, from the Learning Parity with Noise assumption in a parameter regime not known to imply public-key encryption. This is evidence against public-key encryption being necessary for sk-PIR.

Under a new conjecture related to the hardness of learning a hidden linear subspace of $\mathbb{F}_2^n$ with noise, we construct sk-PIR with similar communication and encoding size $|\hat x|=(1+\epsilon)\cdot N$ in which the server is implemented by a Boolean circuit of size $(4+\epsilon)\cdot N$. This is the first candidate PIR scheme with such a circuit complexity.

The scalability of modern decentralized blockchain systems is constrained by the requirement that the participating nodes execute the entire chains transactions without the ability to delegate the verification workload across multiple actors trustlessly. This is further limited by the need for sequential transaction execution and repeated block validation, where each node must re-execute all transactions before accepting blocks, also leading to delayed broadcasting in many architectures.

Consequently, throughput is limited by the capacity of individual nodes, significantly preventing scalability.

In this paper, we introduce GIGA, a SNARK-based protocol that enables trustless parallel execution of transactions, processing non-conflicting operations concurrently, while preserving security guarantees and state consistency. The protocol organizes transactions into non-conflicting batches which are executed and proven in parallel, distributing execution across multiple decentralized entities. These batch proofs are recursively aggregated into a single succinct proof that validates the entire block.

As a result, the protocol both distributes the execution workload and removes redundant re-execution from the network, significantly improving blockchain throughput while not affecting decentralization.

Performance estimates demonstrate that, under the same system assumptions (e.g., consensus, networking, and virtual machine architecture) and under high degrees of transaction parallelism (i.e., when most transactions operate on disjoint parts of the state), our protocol may achieve over a 10000x throughput improvement compared to popular blockchain architectures that use sequential execution models, and over a 500x improvement compared to blockchain architectures employing intra-node parallelization schemes.

Furthermore, our protocol enables a significant increase in transaction computational complexity, unlocking a wide range of use cases that were previously unfeasible on traditional blockchain architectures due to the limited on-chain computational capacity.

Additionally, we propose a reward mechanism that ensures the economic sustainability of the proving network, dynamically adjusting to computational demand while fostering competition among provers based on cost-efficiency and reliability.

Screaming-channel attacks enable Electromagnetic (EM) Side-Channel Attacks (SCAs) at larger distances due to higher EM leakage energies than traditional SCAs, relaxing the requirement of close access to the victim. This attack can be mounted on devices integrating Radio Frequency (RF) modules on the same die as digital circuits, where the RF can unintentionally capture, modulate, amplify, and transmit the leakage along with legitimate signals. Leakage results from digital switching activity, so the hypothesis of previous works was that this leakage would appear at multiples of the digital clock frequency, i.e., harmonics. This work demonstrates that compromising signals appear not only at the harmonics and that leakage at non-harmonics can be exploited for successful attacks. Indeed, the transformations undergone by the leaked signal are complex due to propagation effects through the substrate and power and ground planes, so the leakage also appears at other frequencies. We first propose two methodologies to locate frequencies that contain leakage and demonstrate that it appears at non-harmonic frequencies. Then, our experimental results show that screaming-channel attacks at non-harmonic frequencies can be as successful as at harmonics when retrieving a 16-byte AES key. As the RF spectrum is polluted by interfering signals, we run experiments and show successful attacks in a more realistic, noisy environment where harmonic frequencies are contaminated by multi-path fading and interference. These attacks at non-harmonic frequencies increase the attack surface by providing attackers with an increased number of potential frequencies where attacks can succeed.

Well-trained deep neural networks (DNN), including large language models (LLM), are valuable intellectual property assets. To defend against model extraction attacks, one of the major ideas proposed in a large body of previous research is obfuscation: splitting the original DNN and storing the components separately. However, systematically analyzing the methods’ security against various attacks and optimizing the efficiency of defenses are still challenging. In this paper, We propose a taxonomy of model-based extraction attacks, which enables us to identify vulnerabilities of several existing obfuscation methods. We also propose an extremely efficient model obfuscation method called O2Splitter using trusted execution environment (TEE). The secrets we store in TEE have O(1)-size, i.e., independent of model size. Although O2Splitter relies on a pseudo-random function to provide a quantifiable guarantee for protection and noise compression, it does not need any complicated training or filtering of the weights. Our comprehensive experiments show that O2Splitter can mitigate norm-clipping and fine-tuning attacks. Even for small noise (ϵ = 50), the accuracy of the obfuscated model is close to random guess, and the tested attacks cannot extract a model with comparable accuracy. In addition, the empirical results also shed light on discovering the relation between DP parameters in obfuscation and the risks of concrete extraction attacks.

We prove the first meta-complexity characterization of a quantum cryptographic primitive. We show that one-way puzzles exist if and only if there is some quantum samplable distribution of binary strings over which it is hard to approximate Kolmogorov complexity. Therefore, we characterize one-way puzzles by the average-case hardness of a uncomputable problem. This brings to the quantum setting a recent line of work that characterizes classical cryptography with the average-case hardness of a meta-complexity problem, initiated by Liu and Pass. Moreover, since the average-case hardness of Kolmogorov complexity over classically polynomial-time samplable distributions characterizes one-way functions, this result poses one-way puzzles as a natural generalization of one-way functions to the quantum setting. Furthermore, our equivalence goes through probability estimation, giving us the additional equivalence that one-way puzzles exist if and only if there is a quantum samplable distribution over which probability estimation is hard. We also observe that the oracle worlds of defined by Kretschmer et. al. rule out any relativizing characterization of one-way puzzles by the hardness of a problem in $\mathbf{NP}$ or $\mathbf{QMA}$, which means that it may not be possible with current techniques to characterize one-way puzzles with another meta-complexity problem.

This paper presents a protocol for scaling the creation, management, and trading of non-fungible tokens (NFTs) on Bitcoin by extending bridgeless minting patterns previously used on other blockchains. The protocol leverages on-chain Bitcoin data to handle all aspects of token ownership, including trading, while integrating a secondary consensus system for minting and optionally modifying token metadata. To minimize its on-chain footprint, the protocol utilizes the OP_RETURN mechanism for ownership records, while complementary NFT-related actions are stored on the LAOS blockchain. All data remains permanently on-chain, with no reliance on bridges or third-party operators.

Typical protocols in the multi-party private set operations (MPSO) setting enable $m > 2$ parties to perform certain secure computation on the intersection or union of their private sets, realizing a very limited range of MPSO functionalities. Most works in this field focus on just one or two specific functionalities, resulting in a large variety of isolated schemes and a lack of a unified framework in MPSO research. In this work, we present an MPSO framework, which allows $m$ parties, each holding a set, to securely compute any set formulas (arbitrary compositions of a finite number of binary set operations, including intersection, union and difference) on their private sets. Our framework is highly versatile and can be instantiated to accommodate a broad spectrum of MPSO functionalities. To the best of our knowledge, this is the first framework to achieve such a level of flexibility and generality in MPSO, without relying on generic secure multi-party computation (MPC) techniques.

Our framework exhibits favorable theoretical and practical performance. The computation and communication complexity scale linearly with the set size $n$, and it achieves optimal complexity that is on par with the naive solution for widely used functionalities, such as multi-party private set intersection (MPSI), MPSI with cardinality output (MPSI-card), and MPSI with cardinality and sum (MPSI-card-sum), in the standard semi-honest model. Furthermore, the instantiations of our framework mainly from symmetric-key techniques yield efficient protocols for MPSI, MPSI-card, MPSI-card-sum, and multi-party private set union (MPSU), with online performance surpassing or matching the state of the art.

At the technical core of our framework is a newly introduced primitive called predicative zero-sharing. This primitive captures the universality of a number of MPC protocols and is composable. We believe it may be of independent interest.

We prove that it is impossible to construct perfect-complete quantum public-key encryption (QPKE) with classical keys from quantumly secure one-way functions (OWFs) in a black-box manner, resolving a long-standing open question in quantum cryptography.

Specifically, in the quantum random oracle model (QROM), no perfect-complete QPKE scheme with classical keys, and classical/quantum ciphertext can be secure. This improves the previous works which require either unproven conjectures or imposed restrictions on key generation algorithms. This impossibility even extends to QPKE with quantum public key if the public key can be uniquely determined by the secret key, and thus is tight to all existing QPKE constructions.

A threshold signature scheme allows distributing a signing key to $n$ users, such that any $t$ of them can jointly sign, but any $t-1$ cannot. It is desirable to prove \emph{adaptive security} of threshold signature schemes, which considers adversaries that can adaptively corrupt honest users even after interacting with them. For a class of signatures that relies on security proofs with rewinding, such as Schnorr signatures, proving adaptive security entails significant challenges.

This work proposes two threshold signature schemes that are provably adaptively secure with rewinding proofs. Our proofs are solely in the random oracle model (ROM), without relying on the algebraic group model (AGM).

- We give a 3-round scheme based on the algebraic one-more discrete logarithm (AOMDL) assumption. The scheme outputs a standard Schnorr signature.

- We give a 2-round scheme based on the DL assumption. Signatures output by the scheme contain one more scalar than a Schnorr signature.

We follow the recent work by Katsumata, Reichle, and Takemure (Crypto 2024) that proposed the first threshold signature scheme with a rewinding proof of full adaptive security. Their scheme is a 5-round threshold Schnorr scheme based on the DL assumption. Our results significantly improve the round complexity.

Katsumata et al.'s protocol can be viewed as applying a masking technique to Sparkle, a threshold Schnorr signature scheme by Crites, Komlo, and Maller (Crypto 2023). This work shows wider applications of the masking technique. Our first scheme is obtained by masking FROST, a threshold Schnorr protocol by Komlo and Goldberg (SAC 2020). The second scheme is obtained by masking a threshold version of HBMS, a multi-signature scheme by Bellare and Dai (Asiacrypt 2021).

Katsumata et al. masked Sparkle at the cost of 2 additional rounds. Our main insight is that this cost varies across schemes, especially depending on how to simulate signing in the security proofs. The cost is 1 extra round for our first scheme, and is 0 for our second scheme.