International Association
for Cryptologic Research

The Oil and Vinegar (OV) trapdoor is widely used in signature schemes such as UOV and MAYO. Recently, Esposito et al. proposed OliVier, an encryption scheme based on this trapdoor. However, the OV trapdoor was originally designed for signatures, and adapting it to encryption introduces inherent challenges.

We identify two such challenges and analyze how OliVier addresses the first, while showing that the unresolved second challenge enables a practical key-recovery attack. We conclude that any scheme using the OV trapdoor for encryption must also solve this second problem, for which no efficient solution is currently known.

Securing sensitive data for long-term storage in the cloud is a challenging problem. Updatable encryption (UE) enables changing the encryption key of encrypted data in the cloud while the plaintext and all versions of the key remain secret from the cloud storage provider, making it an efficient alternative for companies that seek to outsource their data storage.

The most secure UE schemes to date follow robust security models, such as the one by Boyd et al. from CRYPTO 2020, and rely exclusively on asymmetric cryptography, thus incurring a substantial performance cost. In contrast, the Nested UE construction of Boneh et al. from ASIACRYPT 2020 achieves much better efficiency with symmetric cryptography, but it provides weaker security guarantees. Boyd et al. further suggest that attaining robust UE security inherently requires the use of asymmetric cryptography.

In this work, we show for the first time that symmetric UE schemes are not inherently limited in their security and can achieve guarantees on par with, and even beyond, Boyd’s UE model. To this end, we extend Boyd’s framework to encompass the class of ciphertext-dependent UE schemes and introduce indistinguishability-from-random (IND\$) as a stronger refinement of indistinguishability. While our IND\$ notion primarily streamlines the proofs of advanced security properties within the model, it yields practical privacy advantages: ciphertexts do not exhibit a recognizable structure that could otherwise distinguish them from arbitrary data.

We then introduce two robustly secure symmetric UE constructions, tailored to different target security levels. Our schemes are built on a novel design paradigm that combines symmetric authenticated encryption with ciphertext re-randomization, leveraging for the first time the use of pseudorandom number generators in a one-time-pad style. This approach enables both robust security and high efficiency, including in AES-based implementations.

Our first scheme, PUE-List, delivers encryption up to 600× faster than prior asymmetric schemes of similar robustness, while matching Boneh et al.’s efficiency and achieving the stronger security level of Boyd et al. Our second scheme, PUE-One, further boosts performance with constant-time decryption 24× faster than all previously known UE schemes, overcoming the main bottleneck in Boneh’s design, while trading off some security, yet still significantly surpassing the guarantees of Boneh’s Nested scheme.

Garbling schemes allow two parties to compute a joint function on private inputs without revealing them. Yet, a semi-honest garbler might exploit hardware/software sidechannel leakages from the evaluator. An alarming threat with no concrete solution yet. Using the homomorphic properties of ElGamal encryption, we can prevent such leakage-based attacks.

Anonymous tokens are cryptographic protocols for restricting the access to online resources to eligible users. After proving eligibility to the token issuer, the client receives a set of tokens. Later, it can prove eligibility to a resource provider by sending one of the tokens received from the issuer. The anonymous token protocol ensures that the resource provider cannot link received tokens to their issuance, even if it colludes with the token issuer. Recently, Faut et al. (EuroS\&P’25) introduced the concept of policy-based anonymous tokens, in which an issuer provides a single pre-token to a client, who can locally derive multiple tokens according to a publicly announced policy. The major advantage of policy-based tokens is that the communication complexity of the issuance phase is constant. While the work of Faut et al. constitutes a promising step in a new direction, their protocol still lacks several desirable properties known from standard anonymous tokens -- most notably, the ability to bind a pre-token and all tokens derived from it to a private metadata bit or a publicly known metadata string. In this work, we present a new framework for policy-based anonymous token schemes in the random oracle model. Our framework includes two concretely practical constructions -- one based on equivalence class signatures and one on algebraic MACs -- as well as a communication-optimized, though less practical, construction based on zkSNARKs. All three constructions can be configured to support private metadata bits, public metadata, or both. We formalize the notion of policy-based anonymous tokens with a private metadata bit and public metadata, and we prove security of the two primary constructions: the equivalence-class-signature-based scheme and the algebraic-MAC-based scheme. Finally, we provide an experimental evaluation and comparison of all our constructions alongside the most relevant related work. Our results demonstrate that our two primary constructions achieve significant efficiency improvements over the scheme of Faut et al., both in terms of computation communication.

We study non-interactive zero-knowledge proofs (NIZKs) for NP satisfying: 1) statistical soundness, 2) computational zero-knowledge and 3) certified-everlasting zero-knowledge (CE-ZK). The CE-ZK property allows a verifier of a quantum proof to revoke the proof in a way that can be checked (certified) by the prover. Conditioned on successful certification, the verifier's state can be efficiently simulated with only the statement, in a statistically indistinguishable way. Our contributions regarding these certified-everlasting NIZKs (CE-NIZKs) are as follows:

- We identify a barrier to obtaining CE-NIZKs in the CRS model via generalizations of known interactive proofs that satisfy CE-ZK.

- We circumvent this by constructing CE-NIZK from black-box use of NIZK for NP satisfying certain properties, along with OWFs. As a result, we obtain CE-NIZKs for NP in the CRS model, based on polynomial hardness of the learning with errors (LWE) assumption.

- In addition, we observe that the aforementioned barrier does not apply to the shared EPR model. Consequently, we present a CE-NIZK for NP in this model based on any statistical binding hidden-bits generator, which can be based on LWE. The only quantum computation in this protocol involves single-qubit measurements of the shared EPR pairs.

High-value custodial systems require both Public Verifiability (PVSS) to audit key distribution and Traceability (TSS) to identify insider leakage via black-box ``reconstruction boxes.'' Existing schemes achieve one property but not both, leaving practical systems exposed to either undetectable dealer misbehavior or untraceable share leakage. Combining these properties introduces the ``Provenance Paradox'': a verifiability-aware reconstruction box with access to verification predicates and public transcripts can reject dummy shares used for tracing because they have no provenance in the public transcript.

We present TSS-PV, the first publicly verifiable traceable secret sharing scheme that resolves this paradox. Our key insight is to inject indistinguishable dummy shares during the sharing phase itself, ensuring they are committed to the public transcript before any reconstruction box is constructed. We formalize syntax and security under a modular adversarial model: public verifiability holds against fully malicious dealers and parties; traceability identifies leaking parties after honest distribution; and non-imputability prevents a malicious dealer from framing honest parties. Both tracing properties assume a verifiability-aware (perfect) reconstruction box.

We instantiate TSS-PV over cyclic groups using Schnorr-based NIZKs and a recent generic tracing framework (CRYPTO'24). Public verification costs scale linearly in the number of parties; tracing costs are quadratic. A Curve25519 prototype on commodity hardware demonstrates practicality: for $32\text{ - }256$ parties, distribution verification completes in $14\text{ - }127$ ms, tracing in $0.24\text{ - }76$ s, and trace verification in $0.15\text{ - }25$ s.

HMAC and its variant NMAC are among the most widely used methods for keying a cryptographic hash function to obtain a PRF or a MAC. Yet, even after nearly three decades of research, their generic PRF security still remains poorly understood, where the compression function of the underlying hash function is treated as a black box and accessible to the adversary. Although a series of works have exploited compression function queries to mount generic attacks, proving tight bounds on the generic PRF security of HMAC and NMAC remains a challenging open question until now.

In this paper, we establish tight bounds on the generic PRF security of HMAC and NMAC. Our bounds capture the influence of the number of construction queries, the number of compression function queries, and the maximal block length of a message on their security. The proofs are carried out in the multi-user setting and the bounds hold regardless of the number of users. In addition, we present matching attacks to demonstrate that our bounds are essentially tight. Taken together, our results close a longstanding gap in the generic PRF security analysis of HMAC and NMAC.

Hamming Quasi-Cyclic (HQC), recently selected by NIST for standardization, does not employ ciphertext compression, unlike its lattice-based counterpart Kyber. In lattice-based encryption, ciphertext compression is a standard post-processing step, typically implemented through coefficient-wise rounding. In contrast, analogous methods have not yet been explored in code-based cryptography. We address this gap by developing techniques to reduce ciphertext sizes in schemes defined over the Hamming metric, with a particular focus on HQC. To support this approach, the decryption failure rate (DFR) analysis is generalized. Specifically, we revisit the modeling of the error that must be correctable with probability $2^{-\lambda}$ to achieve $\lambda$ bits of security; previously, only tractable under an independence assumption. We propose a more accurate model of the error distribution, which takes dependencies between the coefficients into account. Confirmed by extensive simulations, the proposed model sharpens the DFR analysis and, hence, our understanding of the security of HQC. Building on this generalized framework, we present a ciphertext compression mechanism that enables a precise DFR analysis and is therefore transparent with respect to security. This is achieved by carefully designing a quantization code with a direct-product structure, aligned with HQC's error-correcting code. For the parameters proposed in the round 4 submission, our techniques reduce HQC ciphertext sizes by up to 4.7%; a proof-of-concept implementation confirms that this improvement comes without noticeable loss in efficiency. Reductions of up to 10% are achievable through a trade-off with public-key size.

We propose a Pairing-based Polynomial Consistency Protocol (PPCP) that verifies the equivalence of polynomial commitments generated under different basis representations, such as the coefficient and Lagrange bases. By leveraging pairing relations, PPCP proves that two commitments correspond to an identical underlying polynomial vector without revealing the polynomial itself. This enables efficient proof aggregation and recursive composition across heterogeneous SNARK systems that adopt distinct polynomial encodings.

Private information retrieval (PIR) is a widely used technique in privacy-preserving applications that enables users to retrieve records from a database without revealing any information about their queries. This study focuses on a type of PIR that has a high ratio between the size of the record retrieved by the client and the server's response. Although significant progress has been made in high-rate PIR in recent years, the computational overhead on the server side remains rather high. This results in low server throughput, particularly for applications involving databases with moderate-size records (i.e. tens of kilobytes), such as private advertising system. In this paper, we present \textsc{Npir}, a high-rate single-server PIR that is based on NTRU encoding and outperforms the state-of-the-art Spiral (Menon \& Wu, S\&P 2022) and NTRUPIR (Xia \& Wang, EuroS\&P 2024) in terms of server throughput for databases with moderate-size records. In specific, for databases ranging from 1 GB to 32 GB with 32 KB records, the server throughput of \textsc{Npir} is 1.50 to 2.84 times greater than that of Spiral and 1.77 to 2.55 times greater than that of NTRUPIR. To improve server throughput without compromising the high-rate feature, we propose a novel tool called NTRU packing, which compresses the constant terms of underlying polynomials of multiple NTRU encodings into a single NTRU encoding, thereby reducing the size of the server's response. Furthermore, \textsc{Npir} naturally supports batch processing for moderate-size records, and can easily handle retrieving for records of varying sizes.tions, we advance secure communication protocols under challenging conditions.

Finding intersections across sensitive data is a core operation in many real-world data-driven applications, such as healthcare, anti-money laundering, financial fraud, or watchlist applications. These applications often require large-scale collaboration across thousands or more independent sources, such as hospitals, financial institutions, or identity bureaus, where all records must remain encrypted during storage and computation, and are typically outsourced to dedicated/cloud servers. Such a highly distributed, large-scale, and encrypted setting makes it very challenging to apply existing solutions, e.g., (multi-party) private set intersection (PSI) or private membership test (PMT).

In this paper, we present Distributed and Outsourced PSI (DO-PSI), an efficient and scalable PSI protocol over outsourced, encrypted, and highly distributed datasets. Our key technique lies in a generic threshold fully homomorphic encryption (FHE) based framework that aggregates equality results additively, which ensures high scalability to a large number of data sources. In addition, we propose a novel technique called \textit{nonzero-preserving mapping}, which maps a zero vector to zero and preserves nonzero values. This allows homomorphic equality tests over a smaller base field, substantially reducing computation while enabling higher-precision representations. We implement DO-PSI and conduct extensive experiments, showing that ours substantially outperforms existing methods in both computation and communication overheads. Our protocol handles a billion-scale set distributed and outsourced to a thousand data owners within one minute, directly reflecting large-scale deployment scenarios, and achieves up to an 11.16$\times$ improvement in end-to-end latency over prior state-of-the-art methods.

Multi-tenant direct-to-cell (D2C) Low Earth Orbit (LEO) satellite networks pose significant risks to users’ location privacy by linking Mobile Network Operator (MNO)- managed identities with Satellite Network Operator (SNO)- visible locations. Existing privacy solutions are ill-suited to the resource-constrained hardware and orbital dynamics of these satellite environments. We present LPG (Location Privacy Game), the first protocol-layer solution offering user-configurable location privacy for D2C LEO. LPG achieves this via identity-location decoupling: SNOs provide connectivity without visibility of user identity, while MNOs manage service and billing without access to precise location information. LPG enables offline secure authentication and key agreement without revealing user identity to satellites, supports user-configurable location disclosure at chosen geographic granularity for essential service needs, and ensures fair billing between MNOs and SNOs through privacy-preserving settlement. Our implementation on a real-world in-orbit LEO satellite and commercial mobile phones demonstrates that LPG is practical and viable in resource-constrained, highly-dynamic LEO environments.

A multi-party private join (MPPJ) protocol enables multiple source parties to provide a receiver party with the inner joins over their respective datasets, while revealing as little information as possible. There is currently no protocol that directly and efficiently enables such a MPPJ beyond the two- or three-party setting. The presently known protocols either achieve weaker functionality (e.g., multi- party private set intersection protocols) or more general ones (e.g., private-join-compute and generic secure multi-party computation protocols) and are therefore more costly to run for the sources. This work formally introduces MPPJ as an explicit goal, and proposes an efficient, helper-assisted protocol that achieves ?-party inner joins with small leakage and close-to-optimal overhead for the sources. Specifically, for ? databases with ? rows, it requires only a single ? (?) upload from the sources to the helper, and a single ? (? · ?) download from the helper to the receiver. Moreover, the helper is entirely oblivious: it enables the efficiency and simplicity goals we are striving for, but it does not learn anything about the computation it facilitates. We formally model and prove the security of our protocol from standard assumptions, in the passive-adversary model. Then, we provide an open-source implementation and an extensive performance evaluation. According to our experiments, our protocol requires 1.02 to 20 times less communication than a current private-join-compute protocol (with no computation over the join) for 2 to 6 parties and input database sizes from 1.5K to 250K records. Finally, we demonstrate the versatility of our approach by extending our protocol to threshold-joins.

Privacy-Preserving Blueprints (PPBs), introduced by Kohlweiss et al. in in EUROCRYPT 2023, offer a method for balancing user privacy and bad-actor detection in private cryptocurrencies. A PPB scheme allows a user to append a verifiable escrow to their transactions which reveals some identifying information to an authority in the case that the user misbehaved. A natural PPB functionality is for escrows to reveal user information if the user sends an amount of currency over a certain threshold. However, prior works constructing PPBs for such a functionality have severe limitations when it comes to efficiency: escrows are either computationally infeasible to compute, or too large to be plausibly stored on a large-scale distributed ledger. We address these gaps by constructing a space and computation-efficient PPB for threshold comparison, producing escrows under 2kb that can be computed in seconds. The scheme can be instantiated using well-known cryptographic primitives, namely variants of the ElGamal encryption scheme and generic non-interactive zero-knowledge proofs. As an additional contribution, we implement one of the theoretical generic PPB constructions originally proposed by Kohlweiss et al. and find that it performs surprisingly well in practice. For the threshold comparison functionality it requires approximately 14kb escrows, and can be computed in around 12 seconds.

This paper presents a Keyword Private Information Retrieval (Keyword PIR) scheme that achieves a constant-factor online computation and communication overhead compared to the underlying Index PIR, bridging the gap between Keyword PIR and Index PIR, and enabling efficient and privacy-preserving queries over diverse databases. We introduce a new Key-Value Store (KVS) instantiate by Minimal Perfect Hash Function, referred to as MPHF-KVS, in which each keyword query requires only a single index query. We then develop a generic Batch PIR framework that converts Index PIR into Keyword PIR using KVS encoding. In particular, when the KVS is instantiated using a Binary Fuse Filter (BFF-KVS), Keyword PIR can be reduced to Batch PIR. Leveraging the updatable hint structure of PIR with side information, we propose a novel {Rewind \& Skip} technique that enables the execution of multiple queries within a single round.

In MPHF-KVS, the online computation and communication costs are at most $2\times$ those of Index PIR. In our Batch PIR with BFF-KVS, building upon three recent PIR schemes with sublinear server-side online computation and communication cost and without extra hint store, our approach inherits their advantages and achieves keyword query costs of less than $7\times$ the cost of an index query, while still maintaining sublinear online complexity.

The wide deployment of the generative pre-trained transformer (GPT) has raised privacy concerns for both clients and servers. While cryptographic primitives can be employed for secure GPT inference to protect the privacy of both parties, they introduce considerable performance overhead. To accelerate secure inference, this study proposes a public decoding and secure verification approach that utilizes public GPT models, motivated by the observation that securely decoding one and multiple tokens takes a similar latency. The client uses the public model to generate a set of tokens, which are then securely verified by the private model for acceptance. The efficiency of our approach depends on the acceptance ratio of tokens proposed by the public model, which we improve from two aspects: (1) a private sampling protocol optimized for cryptographic primitives and (2) model alignment using knowledge distillation. Our approach improves the efficiency of secure decoding while maintaining the same level of privacy and generation quality as standard secure decoding. Experiments demonstrate a $2.1\times \sim 6.0\times$ speedup compared to standard decoding across three pairs of public-private models and different network conditions.

Transformer models have gained significant attention due to their power in machine learning tasks. Their extensive deployment has raised concerns about the potential leakage of sensitive information during inference. However, when being applied to Transformers, existing approaches based on secure two-party computation (2PC) bring about efficiency limitations in two folds: (1) resource-intensive matrix multiplications in linear layers, and (2) complex non-linear activation functions like $\mathsf{GELU}$ and $\mathsf{Softmax}$. This work presents a new two-party inference framework $\mathsf{Nimbus}$ for Transformer models. For the linear layer, we propose a new 2PC paradigm along with an encoding approach to securely compute matrix multiplications based on an outer-product insight, which achieves $2.9\times \sim 12.5\times$ performance improvements compared to the state-of-the-art (SOTA) protocol. For the non-linear layer, through a new observation of utilizing the input distribution, we propose an approach of low-degree polynomial approximation for $\mathsf{GELU}$ and $\mathsf{Softmax}$, which improves the performance of the SOTA polynomial approximation by $2.9\times \sim 4.0\times$, where the average accuracy loss of our approach is 0.08\% compared to the non-2PC inference without privacy. Compared with the SOTA two-party inference, $\mathsf{Nimbus}$ improves the end-to-end performance of BERT inference by $2.7\times \sim 4.7\times$ across different network settings.

The sum-check protocol is a fundamental building block in succinct arguments. However, its security formalization is often tightly coupled with the larger protocol in which it is embedded, making modular design and analysis challenging. To address this limitation, we introduce \emph{functional proof systems (FPS)}, generalizing interactive proof systems by viewing the verifier as a function parameterized by the prover, and defining security by asserting properties of this function. This novel perspective not only enables a clean, self-contained security definition for sum-check, but also opens possibility for designing and formalizing security of protocols that prove statements dynamically generated during the protocol---both tasks are difficult for traditional proof systems.

We develop a framework for composing multiple FPSs by executing them in parallel, and analyzing security of the composite protocol. We apply this framework to analyze existing protocols, including BaseFold, a popular polynomial commitment scheme, and BulletProofs, a well-known inner product argument, providing more modular and simpler security proofs than their original analysis. Particularly for BaseFold, our security proof avoids the need for introducing a new variant of correlated agreement theorem, thus building its security directly on the well-studied theorems of FRI. Finally, we construct a new transparent, pairing-free, doubly efficient, and homomorphically additive polynomial commitment scheme by composing existing protocols, demonstrating the practical utility of our framework for designing novel cryptographic schemes.

Searchable Encryption (SE) has shown a lot of promise towards enabling secure and efficient queries over encrypted data. In order to achieve this efficiency, SE inevitably leaks some information, and a big open question is how dangerous this leakage is. While prior reconstruction attacks have demonstrated effectiveness in one-dimensional settings, extending them to high-dimensional datasets remains challenging. Existing methods either demand excessive query information (e.g. an attacker that has observed all possible responses) or produce low-quality reconstructions in sparse databases. In this work, we present REMIN, a new leakage-abuse attack against SE schemes in multi-dimensional settings, based on access and search pattern leakage from range queries. Our approach leverages unsupervised representation learning to transform query co-occurrence frequencies into geometric signals, allowing the attacker to infer relative spatial relationships between records. This enables accurate and scalable reconstruction of high-dimensional datasets under minimal leakage. We begin with a passive adversary that persistently observes all encrypted queries and responses, and later extend our analysis to an more active attacker capable of poisoning the dataset. Furthermore, we introduce REMIN-P, a practical variant of the attack that incorporates a poisoning strategy. By injecting a small number of auxiliary anchor points REMIN-P significantly improves reconstruction quality, particularly in sparse or boundary regions. We evaluate our attacks extensively on both synthetic and real-world structured datasets. Compared to state-of-the-art reconstruction attacks, our reconstruction attack achieves up to 50% reduction in mean squared error (MSE), all while maintaining fast and scalable runtime. Our poisoning attack can further reduce MSE by an additional 50% on average, depending on the poisoning strategy.

On a blockchain, users compete for scarce block space in an auction run by the miner to get their transactions confirmed in the block. This auction is called transaction fee mechanism (TFM). Recent work [Rou21, CS23, SCW23] has been focused on incentive compatibility (IC), requiring that honest behavior maximizes the payoff for each type of strategic player: users, the miner, or miner–user coalitions. In this work, we introduce rational-harm proofness (RHP), which rules out any deviation that harms honest parties without also reducing the deviator’s own utility. RHP closes a gap left by IC: IC does not forbid utility neutral yet externally harmful deviations. For example, in a second-price auction, the second-highest bidder can increase the winner’s payment without affecting their own payoff. Such deviation is eliminated by RHP.

We characterize TFMs satisfying RHP alongside incentive compatibility for users (UIC) and miners (MIC). For finite block size, we develop a complete characterization in two models: - In the plain model —where a single miner unilaterally implements the auction—we prove a tetrilemma (3-out-of-4 impossibility): among the four desired properties positive miner revenue, UIC, MIC, RHP against miner–user coalitions, no mechanism achieves all four simultaneously. Meanwhile, any three are jointly achievable in the plain model. - In the MPC-assisted model —where a committee of miners jointly implement the auction via multi-party computation (MPC)—we construct a randomized TFM with a positive miner revenue that achieves UIC, MIC, and RHP against all three types of strategic players. We further show that randomness is necessary: any deterministic TFM satisfying UIC and RHP in this model must confirm no transactions when the number of users exceeds the block size.

Finally, we show that IC and RHP are incomparable: for each strategic role, there are mechanisms satisfying one but not the other in both models. Our results broaden the design objectives for TFMs: beyond incentive compatibility, mechanisms should also preclude costless harm to honest participants