Token-weighted voting is widely used in DAO governance, but public voting weights together with weighted tallies can reveal identifiable voters' choices. Publishing only the final outcome reduces this disclosure, yet an output policy alone does not prevent a privileged participant from reconstructing the exact weighted tally during computation. We present a verifiable winner-only tally-hiding construction for weighted binary voting. Registered weights are bound to credentials in...

Function secret sharing (FSS) gives two parties succinct keys whose local evaluations add up to a hidden function value. Removing the dealer is the natural next step for preprocessing-based secure computation, but malicious security then requires more than checking a finished key: the generation transcript, the function the key actually computes, and every released evaluation share must all be bound to one execution. We call this the cross-phase binding problem, and we solve it. We...

Candidates for post-quantum migration carry additional costs compared to their pre-quantum counterparts, especially for signatures, and they lose attractive properties of schemes such as BLS: homomorphism, and hence direct signature aggregation. We propose a methodology through which a pre-quantum primitive may still be securely used past Q-day (the advent of quantum computers) in settings where forgery of signatures or cryptographic proofs need only be prevented for a bounded lifespan...

Anonymity infrastructures such as mix networks, anonymous storage, and privacy-preserving replication rely on public re-randomization: any party holding only public information can transform a ciphertext into a fresh-looking encryption of the same plaintext, hiding the linkage between the two. Classical ElGamal-based solutions are broken by quantum adversaries, while existing lattice-based alternatives carry very large ciphertexts with unanalyzed noise growth, rely on heavyweight...

Anonymous communication systems aim to hide which user sent which message. Existing designs span efficient mixnets that rely on at least one honest mix server and decentralized protocols such as Dining Cryptographers networks (DC-nets) or secure multi-party computation (MPC)-based shuffles, which typically require greater communication or interaction. We introduce \emph{verifiable self-mix} (VSM), an anonymity architecture for privately placing messages in a public bulletin-board...

Multi-signatures with key aggregation provide compact signatures verifiable under a single aggregated public key, but protect signer privacy only when public keys are used in a one-time manner. To address this limitation, recent privacy-preserving constructions provide stronger privacy guarantees under public-key reuse. However, they only guarantee privacy in the signature-only setting, where adversaries observe only the final aggregated public key and signature. In practical deployments,...

Private Set Intersection (PSI) enables parties to compute the intersection of their input item sets while preserving privacy. In many real-world applications, however, each item is accompanied by a sensitive weight, and the ability to privately compute over such weights is crucial. Existing research in this direction is fragmented and driven by application-specific goals, with representative examples including PI-Sum (computing the sum of weights over the intersection), inner-product Private...

We present Mu-qt-PEGASIS, a mathematically precise con- ditional compiler for interactive aggregate signatures built from the qt-PEGASIS effective class-group action. Our central observation is that the torsor structure of the public-key space blocks the standard Schnorr- or BLS-style verification equations used in conventional aggregate sig- natures. We resolve this by separating verification into two layers: a proof-authentication layer that certifies public-key registration and...

Batched identity-based encryption (batched IBE), introduced by Agarwal, Fernando, and Pinkas (CRYPTO 2025), enables a key authority to issue a single succinct key that decrypts an entire batch of ciphertexts. However, existing batched encryption protocols support only identity-based access control and do not accommodate expressive attribute-based policies. We are the first to introduce Batched Attribute-Based Encryption (B-ABE), a generalization of batched encryption to the attribute-based...

The California Consumer Privacy Act (CCPA) grants consumers the right to request deletion of personal data held by registered data brokers. California’s 2023 Delete Act provides a new mechanism through which consumers can exercise this right by leveraging a centralized Delete Request and Opt-out Platform (DROP), enabling users to submit a single request that must be periodically processed by all registered data brokers. Since this platform aggregates sensitive user information, it is...

We present key-recovery attacks on the constructions of TALUS (Kao and Chang), a threshold ML-DSA (FIPS 204) construction available on arXiv and scheduled for presentation at the NIST Threshold Call Preview Talks Round 2 (TCPT-2, https://csrc.nist.gov/events/2026/tcpt). For TALUS-MPC, which is claimed EUF-CMA secure against an adversary corrupting up to $T − 1$ parties, we show the claim is false via two independent attacks, both exploiting the same root cause. TALUS-MPC uses Feldman...

Federated Learning (FL) enables collaborative model training without centralizing raw data, but remains vulnerable to gradient inference attacks, malicious aggregation servers, and communication inefficiencies. Existing cryptographic secure aggregation schemes provide confidentiality and verifiability yet lack formal statistical privacy guarantees, while most differential privacy (DP)-based approaches rely on fixed noise injection, resulting in suboptimal privacy--utility tradeoffs. This...

Secure/Multipurpose Internet Mail Extensions (S/MIME) is a standards-based mechanism for certificate-backed email signing and encryption. Its post-quantum migration is now technically actionable: ML-KEM public keys can be represented in X.509 certificates, and CMS can carry ML-KEM recipient information through \texttt{KEMRecipientInfo}. These standards solve an encoding problem, but they do not by themselves solve an assurance problem. A mailbox may possess a post-quantum-capable certificate...

We study the problem of certifying statistical claims over datasets contributed by multiple independent sources. In this setting, an untrusted server aggregates signed data records and publishes claims such as sums, averages, or rates, while any third party can verify that these claims are correct with respect to the authenticated input data, without needing access to the underlying records. A central challenge is to achieve public verifiability without requiring trust in the aggregator,...

Integrating Real-World Assets (RWA) into Decentralized Finance (DeFi) enables borrowers to leverage off-chain assets for on-chain transactions. However, this integration faces a critical security challenge: ensuring that private asset data, utilized within cryptographic proofs, is authenticated by and cryptographically bound to a trusted issuer. Existing solutions, such as decentralized identifiers (DIDs) and oracles, fail to simultaneously satisfy the requisite security standards and...

We study transparent public generation of hard supersingular curves: a public, seeded, rerunnable algorithm outputs a supersingular curve while exposing the seed, verification transcript, and all algebraic information reconstructible from the implementation. This setting is distinct from trusted or distributed ceremonies, where a witness may be hidden, erased, or zero-knowledge protected. We define a transcript-security model for this setting and develop barriers for several modeled...

Asynchronous multi-party computation (AMPC) en- ables a set of mutually distrustful parties to securely compute any joint function on their private inputs under arbitrary network delays. The guaranteed output delivery (i.e., G.O.D.) property is essential for availability, but can be very challenging to achieve in practice. HoneyBadgerMPC (CCS 2019), the first practical AMPC protocol, follows Beaver’s paradigm that generates Beaver triples offline to simplify online computation. One...

Rate Limiting Nullifier (RLN) is a privacy-preserving and decentralized spam-prevention mechanism for anonymous broadcast networks: each member can emit at most $r$ signals per epoch, and any violation reveals a secret that enables the member's stake to be slashed. The standard construction binds each membership to a single secret key $\mathsf{sk}_G$, so the unit of identity, the unit of authorization, and the unit of slashing all coincide with one party. This rules out settings in which a...

Federated fine-tuning of Large Language Models via Low Rank Adaptation (LoRA) faces a critical privacy-efficiency trade-off: low-rank factors can leak sensitive data, yet standard secure aggregation is restricted to linear operations. Existing solutions for aggregating matrix products (e.g., $\mathbf{B}_i \mathbf{A}_i$) either sacrifice exactness, depend on a trusted third party, or incur prohibitive costs at scale. We present SecLoRA, the first decentralized framework achieving exact...

Synchronized multi-signatures allow for non-interactive aggregation of signatures generated within the same time step. This primitive is particularly well-suited for high-throughput blockchain protocols like Ethereum, where many distributed signers must validate the same block within a synchronized slot. In this work, we present Lemur, a post-quantum synchronized multi-signature from (module) lattices that improves upon the state-of-the-art in efficiency, scalability, and flexibility. Lemur...

Functional adaptor signatures (FAS) are a novel cryptographic primitive introduced at CCS'24 that enable privacy-preserving, fine-grained data-payment exchanges between a seller and a buyer in a trustless and atomic manner. In this setup, the seller holds sensitive data \(x\) (e.g., patient records, climate data), and the buyer specifies a function \(f\) (e.g., aggregate, sum). FAS guarantees that the buyer learns \(f(x)\) (and nothing beyond) if and only if the seller receives payment in...

Publicly verifiable secret sharing (PVSS) is a fundamental primitive in threshold cryptography that allows a dealer to share a secret $S$ among a set of $n$ parties via a publicly verifiable transcript. Any subset of $t+1$ parties can then use their individual shares to reconstruct the full secret $S$, whereas $t$ or fewer shares give no information about $S$. As such, the secret $S$ remains hidden from an adversary that corrupts up to $t$ parties. Recently, Bacho and Loss (CCS 2023) gave...

Authenticated Private Information Retrieval (Authenticated PIR) allows the client to retrieve the desired database entry without revealing any information about the query, while safely aborting if malicious behavior by the server is detected (presented in USENIX '23). However, two key challenges remain: existing single-server authenticated PIR schemes with sublinear online communication have not yet been clearly and fully implemented; incremental updates to the digest introduce unnecessary...

Registered attribute-based encryption (ABE) is a generalization of ABE that replaces the central trusted key-issuer with an untrusted key curator. In registered (ciphertext-policy) ABE, users generate their own public keys and there is a transparent aggregation process that takes the public keys of the users together with their attributes and aggregates them into a short master public key that functions as the public key for a standard ABE scheme. A sequence of works has focused on...

Multi-signatures allow many signers to jointly generate a single (short) signature on a message. In this context, we introduce doubly aggregatable signatures, a new primitive that consists of two sets of signers. It enables a layer-1 signer to create a succinct attestation, called the layer-1 signature, to the set of observed layer-0 signatures on the same message, and publicly aggregate many layer-1 signatures into a succinct certificate. This certificate can be verified against the public...

In 2020, Ravi et al. [23] published three shuffling variants with each offering a different performance-security trade-off for protecting the Numeric theoretic Transform (NTT). Among them, the fine-shuffling was proposed as the lightweight variant. The idea is to randomise the order of loading and storing the operands of the butterfly computation using conditional swapping based on random control bit. However, as noted by the authors themselves, basic fine-shuffling implementation suffered...

Trustworthy federated learning requires both update privacy and aggregate integrity. While secure aggregation protects the confidentiality of client updates, it does not prevent a malicious server from tampering with the final aggregate. Existing verifiable schemes typically address this limitation by introducing separate integrity-verification layers, such as zero-knowledge proofs, homomorphic hashes, and commitmentbased mechanisms. However, these approaches either require clients to remain...

We present a new framework for constructing practically efficient publicly verifiable secret sharing(PVSS) with non-interactive dealers, in that the dealer may go offline after sending a single message, and is not involved in the share verification process. We use identity-based encryption (IBE) and commitments as the main ingredients and avoid expensive zero-knowledge proofs. Instantiating them with post-quantum secure schemes, a lattice-based IBE and a hash-based commitment, we obtain our...

The rapid proliferation of privacy-sensitive machine learning and scientific computing applications has intensified the demand for secure multi-party computation (MPC) protocols that can efficiently evaluate complex nonlinear functions. Despite four decades of research, MPC systems face two fundamental barriers: heavy offline preprocessing and limited support for transcendental functions. Existing preprocessing-free frameworks are confined to comparison-related functions (e.g., Bicoptor) and...

We propose a new private telemetry system for computing t-heavy hitters in the STAR (Davidson et al., CCS 2022) and POPSTAR (Li et al., USENIX 2024) model. In this setting, each client generates a report with the assistance of a lightweight Randomness Server and submits it to a central Aggregation Server, which can then locally compute only the heavy hitters. As compared to STAR and POPSTAR---which reveal either the full (pseudonymized) frequency histogram or a complex function of it---our...

In a (non-interactive) multi-signature scheme, parties independently generate keys and produce signatures on a common message, which can be aggregated into a single signature verifiable with respect to the set of public keys. Existing multi-signature constructions, however, suffer from at least one of two limitations: either (1) aggregate signatures cannot be further aggregated, or (2) verification requires auxiliary information beyond the set of public keys, such as the aggregation...

Graph neural network parameters are valuable intellectual property, and high-fidelity extraction enables model stealing, evasion, and downstream abuse. Prior cryptanalytic extraction can recover exact parameters in hard-label settings, yet existing methods focus on feedforward models and do not extend to message-passing graph neural networks. The obstacles include coupled queried nodes, hidden aggregation operators, and sign and scale ambiguities created by message passing. To our knowledge,...

Registration-Based Encryption (RBE) is a public-key encryption mechanism which allows a user to register their identity (e.g. email address) and self-generated public key with a key curator (e.g. an organisation). The key curator aggregates these keys into a compact digest. Using only this digest and the recipient’s identity, anyone can encrypt messages to any registered user. As the key curator is not entrusted with any secrets, RBE presents a solution to the key escrow problem, which...

Polynomial commitment schemes (PCSs) are a fundamental cryptographic primitive that allows a prover to reveal evaluations for a committed polynomial. Motivated by the inefficiency of proof generation for large-scale computations as well as the concerns regarding third-party reliance and quantum threats, a line of recent works has focused on distributing code-based PCS, where the proving workload is distributed among multiple sub-provers to accelerate proof generation, while preserving...

We present a new method for merging short computationally sound proofs (SNARGs). Intuitively, this means going from short proofs of two statements to a short proof of a statement that is a ``logical consequence'' of the two. Our work is the first to handle an unbounded polynomial number of recursive merges with arbitrary topology. Unlike prior works, the running time of our security reduction grows only with the depth of the merging process, and not with the associated ``tree size",...

Decentralized Finance (DeFi) lending protocols implement programmable credit markets without intermediaries. This paper systematizes the DeFi lending ecosystem, spanning collateralized lending (including over- and under- collateralized designs, and zero-liquidation loans), uncollateralized primitives (e.g., flashloans), and yield aggregation protocols which allocate capital across underlying lending platforms. Beyond a taxonomy of mechanisms and comparing protocols, we provide empirical...

In federated learning (FL), secure aggregation (SA) allows a server to compute aggregate model updates (gradients) without accessing individual client gradients. SA is intended to protect clients’ local dataset from being inferred through individual gradients. However, recent NDSS 2025 work shows that even state-of-the-art SA protocols can be vulnerable, as a malicious server may reconstruct clients’ datasets from aggregated gradients. This demonstrates that protecting dataset privacy...

Privacy-preserving data aggregation has become a fundamental tool for large-scale analytics in AI-driven and cloud-based systems. While existing solutions provide the default privacy guarantee, i.e., input confidentiality, most assure a semi-honest adversary model and fail to simultaneously ensure user anonymity, selective disclosure, and result privacy in the multiple data customers environment. In this work, we introduce PRIVADA, a maliciously secure data aggregation solution that uses MPC...

Multi-signatures are a fundamental cryptographic primitive in distributed systems, enabling a set of parties to jointly produce a compact signature on a common message. Of particular interest are constructions instantiated over pairing-free cyclic groups with a two-round signing protocol, as such schemes offer improved efficiency and deployability in practice. Support for key aggregation is an additional highly desirable property, allowing multiple public keys to be combined into a single...

Privacy‑preserving aggregation is a cornerstone for AI systems that learn from distributed data without exposing individual records, especially in federated learning and telemetry. Existing two‑server protocols (e.g., Prio and successors) set a practical baseline by validating inputs while preventing any single party from learning users’ values, but they impose symmetric costs on both servers and communication that scales with the per‑client input dimension $L$. Modern learning tasks...

We present Aggios, a scalable and privacy preserving proxy voting system designed for frequent and large-scale elections such as Decentralized Autonomous Organizations (DAO), when storing votes on the bulletin board is expensive. To this end, Aggios introduces `aggregators': entities to which voters delegate their votes, and who then post their batched proofs on the public ledger. Aggios achieves strong integrity guarantees: only authorized voters can vote, votes are counted correctly,...

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...

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...

The distributed nature of federated learning systems makes them vulnerable to backdoor attacks in which malicious clients manipulate local training data using trigger-dependent behaviors to cause targeted misclassification. Although homomorphic encryption preserves the privacy of model updates during aggregation, it limits the application of conventional defenses that require access to plaintext updates. Moreover, distinguishing poisoned models from benign variations becomes more challenging...

Federated Learning is an emerging machine learning paradigm that enables distributed model training directly at data sources and transmitting only model updates, thereby reducing communication bottlenecks and mitigating risks associated with raw data exposure. Despite these advantages, recent advances have demonstrated that privacy in federated learning remains limited and subject to inference attacks that exploit shared model updates to extract sensitive information. To address this...

Collaborative zkSNARKs allow multiple mutually distrustful parties to jointly prove the correctness of a computation without revealing their private inputs. This enables a new class of exciting secure applications, such as privacy-preserving healthcare data aggregation, privacy preserving audits, and jointly trained machine learning models. Unfortunately, existing collaborative zkSNARKs still struggle to support many target applications in practice, which operate over large-scale datasets....

Secure multiparty computation (MPC) enables distrusting parties in a distributed system to compute on their private inputs without compromising their privacy. For many secret-sharing-based approaches, including some of today's most efficient MPC protocols, there is a pattern where two shared values are locally multiplied into some intermediate representation that is immediately and interactively translated back into sharings of the product. The intermediate representation is often still a...

Secure aggregation is a core primitive for privacy-preserving federated learning, enabling a server to compute aggregates of client updates without learning individual inputs. Recent protocols have explored committee-based designs to reduce client overhead and tolerate weakly connected participants. However, existing approaches still incur communication and computation costs that scale with the number of clients and/or the size of model updates. This becomes a serious bottleneck in...

Federated Learning (FL) is a collaborative Machine Learning (ML) process where clients locally train an ML model on their private inputs, and send it to a server that aggregates the local model updates to obtain a global model update. FL is widely used in applications where the training data is distributed among several clients, e.g., for next word prediction in Google keyboard (Gboard). Nevertheless, FL faces several challenges concerning privacy and security. 1) Client privacy needs to be...

Secure aggregation enables a server to learn the sum of private inputs of clients, while revealing no additional information beyond the final sum. Recent work, Willow (CRYPTO 2025) achieves one--shot secure aggregation in the single-server model with dynamic client participation. To ensure security under these features, Willow relies on an auxiliary committee to verify the correctness of the aggregation. Although this verification requires no private information---broadening the set of...

Bitcoin Improvement Proposal 327 specifies a variant of the MuSig2 multi-signature protocol that is becoming widely adopted in Bitcoin applications. This protocol enables multiple participants to collaboratively compute (BIP 340) Schnorr signatures for a single aggregate public key efficiently, while preventing external parties from distinguishing whether multiple signers were involved. It has been widely proposed that it should be secure to allow MuSig2 participant keys to themselves be...

Incremental preprocessing in private information retrieval (PIR) schemes refers to handle insertions, modifications, and deletions to the database without requiring complete preprocessing after each update. This broadens the applicability of PIR in practical scenarios. However, two major issues remain: the concept of incremental preprocessing for the single-server PIR is still not established, and the row-level update strategy (iSimplePIR (Row-level)) introduces excessive unnecessary...

Accurate measurements of user engagement underpin important decisions in various settings, such as determining advertising fees based on viewership of online content, allocating public funding based on a clinic’s reported patient volume, or determining whether a group chat app disseminated a message without censorship. While common, self-reporting is inherently untrustworthy due to misaligned incentives (e.g., to inflate). Motivated by this problem, we introduce the notion of Verifiable...

Collecting statistics from users of software and online services is crucial to improve service quality, yet obtaining such insights while preserving individual privacy remains a challenge. Function secret sharing (FSS) is a promising tool for this problem. However, FSS-based solutions still face several challenges for streaming analytics, where messages are continuously sent, and secure computation tasks are repeatedly performed over incoming messages. We introduce a new cryptographic...

Federated Learning (FL) is an advancement in Machine Learning motivated by the need to preserve the privacy of the data used to train models. While it effectively addresses this issue, the multi-participant paradigm on which it is based introduces several challenges. Among these are the risks that participating entities may behave dishonestly and fail to perform their tasks correctly. This misbehavior, in turn, also threatens privacy, because an undetected deviation in training or...

Data Availability Sampling (DAS) has emerged as a key scalability technique for blockchain systems, enabling light clients to verify that block data have been fully published without downloading them in their entirety. We introduce FRIVail, a new DAS construction built on top of the FRI-Binius polynomial commitment scheme, designed for datasets composed of many independent single-row payloads that together form a block’s data blob. FRIVail exploits the intrinsic Reed–Solomon structure of...

We introduce a simple yet novel framework for privacy-preserving machine learning inference that allows a client to query multiple models without a trusted third party aggregator by leveraging homomorphically encrypted model evaluation and multi-party computation. This setting allows for dispersed training of models such that a client can query each separately, and aggregate the results of this `ensemble inference'; this avoids the data leakage inherent to techniques that train collectively...

Homomorphic tallying in secure e-voting protocols enables privacy-preserving vote aggregation. For this approach, zero-knowledge proofs (ZKPs) for ensuring the validity of encrypted ballots are an essential component. While it has been common to construct tailored ZKPs for every kind of ballot and voting method at hand, recently Huber et al. demonstrated that also general-purpose ZKPs (GPZKPs), such as Groth16 zkSNARKs, are suited for checking ballot validity. Unlike tailored solutions,...

We present arya-STARK, a unified post-quantum secure framework that enables Aggregation-Robust Yet Authentic training in Federated Learning through transparent zk-STARK proofs. Current federated learning deployments remain vulnerable to malicious or Byzantine clients capable of submitting statistically valid yet adversarial gradients, while also relying on quantum-fragile primitives for authentication. arya-STARK bridges these gaps by combining (i) transparent, hash-based zk-STARK proofs to...

Remote attestation is a fundamental security mechanism for assessing the integrity of remote devices. In practice, widespread adoption of attestation schemes is hindered by a lack of public verifiability and the requirement for interaction in existing protocols. A recent work by Ebrahimi et al. (NDSS'24) constructs publicly verifiable, non-interactive remote attestation, disregarding another important requirement for attesting sensitive systems: privacy protection. Similar needs arise in...

Zero-knowledge virtual machines (zkVMs) must correctly model all edge cases of lowlevel machine instructions, including division and remainder, while keeping algebraic constraints simple enough for efficient STARK proving. This work presents a focused but meaningful redesign of the DivRemChip used in the SP1 zkVM (as adapted to rWasm) to implement WASM’s 32-bit division and remainder instructions (i32.div{u,s}, i32.rem{u,s}) over the BabyBear field. Our chip remains local (single-row AIR)...

Multi-signatures enable multiple parties to create a joint signature on the same message. Such schemes aggregate several individual signatures and public keys into a short signature and aggregated public key, and verification is performed on these combined values. Interestingly, all existing notions of unforgeability for multi-signatures are designed with a single honest user in mind, overlooking the multi-user setting that multi-signatures are built for. While multi-user security can be...

Secure coin-tossing is typically modeled as an input-less functionality, where parties with no private inputs jointly generate a fair coin. In the dishonest majority setting, however, a strongly fair coin-tossing protocol is impossible. To circumvent this barrier, recent work has adopted the weaker notion of game-theoretic fairness, where adversaries are rational parties with preferences for specific outcomes, seeking to bias the coin in their favor. Yet these preferences may encode secret...

Private BitTorrent trackers enforce upload-to-download ratios to prevent free-riding, but suffer from three critical weaknesses: reputation cannot move between trackers, centralized servers create single points of failure, and upload statistics are self-reported and unverifiable. When a tracker shuts down, users lose their contribution history and cannot prove their standing to new communities. We address these problems by storing reputation in smart contracts and replacing self-reports with...

Proposer-Builder Separation (PBS) in Ethereum improves decentralization and scalability by offloading block construction to specialized builders. In practice, MEV-Boost implements PBS via a side-car protocol with trusted relays, resulting in increased centralization as well as security and performance concerns. We propose Decentralized Proposer-as-a-Service (DPaaS), a deployable architecture that eliminates relays while preserving compatibility with Ethereum’s consensus layer. Our insight is...

Hash-based signatures are a strong candidate for post-quantum scenarios requiring authentication and integrity. Their security relies only on (well-studied) properties of hash functions, so they may be thought as being more robust than other schemes that (today) resist quantum attacks, like those based on lattices, coding or isogenies. Recent works also study hash-based signature schemes with additional properties, such as group, ring, threshold, or aggregate signature schemes. In this...

Cryptographic proofs are a versatile primitive. They are useful in practice not only when used as a standalone tool (for example in verifiable computation), but also when applied $\textit{on top}$ of other cryptographic functionalities — hash functions, signature schemes, and even proofs themselves — to $\textit{enhance}$ their security guarantees (for example to provide succinctness). However, when the security of the other primitive is established in the Algebraic Group Model (AGM), the...

Random beacons play a critical role in blockchain protocols by providing publicly verifiable, unpredictable randomness essential for secure assignment of protocol roles such as block producers and committee membership. In the interest of efficiency, many deployed blockchains adopt beacon algorithms that suffer from grinding: an adversarial attack in which a party exploits freedom given by the protocol to bias the outcome of the random beacon by resampling it several times and picking the...

Aggregate signatures allow compressing multiple single-signer signatures into a single short aggregate signature. This primitive has attracted new attention due to applications in blockchains and cryptocurrencies. In multisig addresses, which is one of such applications, aggregate signatures reduce the sizes of transactions from multisig addresses. Security of aggregate signatures under adaptive corruptions of signing keys is important, since one of the motivations of multisig addresses was...

Threshold signatures are an important tool for trust distribution, and preserving the interface of standardized signatures, such as Schnorr signatures, is crucial for their adoption. In practice, latency dominates end-to-end signing time, so minimizing the number of interaction rounds is critical. Ideally, this is achieved under minimal assumptions and with adaptive security, where the adversary can corrupt signers on-the-fly during the protocol. While Schnorr signatures are proven...

LOL (League of Legends) is a general framework for designing blockwise stream ciphers to achieve higher software performance in 5G/6G. Following this framework, stream ciphers LOL-MINI and LOL-DOUBLE are proposed, each with a 256-bit key. This paper focuses on analyzing their security against correlation attacks. Additionally, we propose an observation on constructing linear approximation trails. The repeated linear approximations in trails may not only increase the number of active S-boxes...

Multi-key fully homomorphic encryption (MK-FHE) enables secure computation over ciphertexts under different keys, but its practicality is hindered by inefficient bootstrapping. In this work, we propose \ours, a new MK-FHE scheme with highly efficient bootstrapping. Our bootstrapping framework improves upon the best-known complexity, reducing it from ${O}(dkn)$ to ${O}(kn)$, and further to ${O}(\sqrt{kn})$ under parallelization, where $d$ is the gadget length (typically scaling with the...

Anonymous credential schemes allow users to prove claims about themselves in a privacy-preserving manner. Put simply, a user can prove that she has an attribute value (e.g., she is a citizen) without revealing any other information about herself. Traditional schemes (including those with multiple credential issuers) operate under the assumption that each recognized issuer produces credentials for all user attributes. This assumption, however, is not practical: in the real world, no such...

We present ORQ, a system that enables collaborative analysis of large private datasets using cryptographically secure multiparty computation (MPC). ORQ protects data against semi-honest or malicious parties and can efficiently evaluate relational queries with multi-way joins and aggregations that have been considered notoriously expensive under MPC. To do so, ORQ eliminates the quadratic cost of secure joins by leveraging the fact that, in practice, the structure of many real queries allows...

We present WISCH, a commit-reveal protocol that combines compact aggregate signatures with hash-based commitments to enable selective disclosure of correlated data in multiparty computation. The protocol separates an on-chain verification core from off chain preparation, so that verification cost depends only on the number of openings, not on the size of the underlying message space. This yields asymptotic efficiency: on-chain cost grows linearly in the number of revealed items and is...

In this work, we introduce an upgrade to Rayls, a novel central bank digital currency (CBDC) design that provides privacy, high performance, and regulatory compliance. In the Rayls construction, commercial banks each run their own (private) ledger and are connected to an underlying decentralized programmable blockchain via a relayer. We introduce an improved and more efficient protocol that allows for efficient anonymous transactions between banks, which is (still) called Enygma. Our...

Secure aggregation enables a central server to compute the sum of client inputs without learning any individual input, even in the presence of dropouts or partial participation. This primitive is fundamental to privacy-preserving applications such as federated learning, where clients collaboratively train models without revealing raw data. We present a new secure aggregation protocol, TACITA, in the single-server setting that satisfies four critical properties simultaneously: (1) one-shot...

An aggregate signature scheme allows a user to take $N$ signatures from $N$ users and aggregate them into a single short signature. One approach to aggregate signatures uses general-purpose tools like indistinguishability obfuscation or batch arguments for NP. These techniques are general, but lead to schemes with very high concrete overhead. On the practical end, the seminal work of Boneh, Gentry, Lynn, and Shacham (EUROCRYPT 2003) gives a simple and practical scheme, but in the random...

Threshold Schnorr signatures enable $t$-out-of-$n$ parties to collaboratively produce signatures that are indistinguishable from standard Schnorr signatures, ensuring compatibility with existing verification systems. While static-secure constructions are well understood and achieve optimal round complexity, obtaining full adaptive security - withstanding up to $t-1$ dynamic corruptions under standard assumptions has proven elusive: Recent impossibility results (CRYPTO’25) either rule out...

The Regular Syndrome Decoding (RSD) problem, introduced nearly two decades ago, is a regular version of the Syndrome Decoding (SD) problem, where the noise vector is divided into consecutive, equally sized blocks, each containing exactly one noisy coordinate. Recently, RSD has gained renewed attention for its applications in Pseudorandom Correlation Generator (PCG) and more. Very recently, several works presented the improved algebraic approach (AGB for short) and ISD approach (including...

We present a real-world deployment of secure multiparty computation to predict political preference from private web browsing data. To estimate aggregate preferences for the 2024 U.S. presidential election candidates, we collect and analyze secret-shared data from nearly 8000 users from August 2024 through February 2025, with over 2000 daily active users sustained throughout the bulk of the survey. The use of MPC allows us to compute over sensitive web browsing data that users would...

This paper addresses the challenge of best arm identification in stochastic multi-armed bandit (MAB) models under privacy-preserving constraints, such as in dynamic spectrum access networks where secondary users must privately detect underutilized channels. While previous network security research has explored securing MAB algorithms through techniques such as homomorphic encryption or differential privacy, these methods often suffer from high computational overhead or introduce noise that...

Zero-knowledge rollups represent a critical scaling solution for Ethereum, yet their practical deployment faces significant challenges in on-chain verification costs. This paper presents a comprehensive implementation of the Tokamak zkEVM verifier, specifically optimized for the BLS12-381 elliptic curve operations introduced by EIP-2537. We detail the complete verification architecture, from EVM compatible data formatting for pairing checks, multi-scalar multiplication (MSM), and elliptic...

Federated learning (FL) proposes to train a global machine learning model across distributed datasets. However, the aggregation protocol as the core component in FL is vulnerable to well-studied attacks, such as inference attacks, poisoning attacks [71] and malicious participants who try to deviate from the protocol [24]. Therefore, it is crucial to achieve both malicious security and poisoning resilience from cryptographic and FL perspectives, respectively. Prior works either achieve...

We present OasisDB, an oblivious and scalable RBDMS framework designed to securely manage relational data while protecting against access and volume pattern attacks. Inspired by plaintext RDBMSs, OasisDB leverages existing oblivious key value stores (KV-stores) as storage engines and securely scales them to enhance per-formance. Its novel multi-tier architecture allows for independent scaling of each tier while supporting multi-user environments without compromising privacy. We demonstrate...

Argument systems are a fundamental ingredient in many cryptographic constructions. The best-performing argument systems to date largely rely on a trusted setup, which is undesirable in trust-minimized applications. While transparent argument systems avoid this trust assumption, they have historically been inefficient, typically exhibiting polylogarithmic proof sizes compared to their trusted counterparts. In 2023, Arun et al. (PKC 2023) constructed the first transparent constant-sized...

Zero-knowledge succinct non-interactive arguments (zkSNARKs) are notorious for their large prover space requirements, which almost prohibits their use for really large instances. Space-efficient zkSNARKs aim to address this by limiting the prover space usage, without critical sacrifices to its runtime. In this work, we introduce Hobbit, the only existing space-efficient zkSNARK that achieves optimal prover time $O(|C|)$ for an arithmetic circuit $C$. At the same time, Hobbit is the first...

Collaborative graph processing refers to the joint analysis of inter-connected graphs held by multiple graph owners. To honor data privacy and support various graph processing algorithms, existing approaches employ secure multi-party computation (MPC) protocols to express the vertex-centric abstraction. Yet, due to certain computation-intensive cryptography constructions, state-of-the-art (SOTA) approaches are asymptotically suboptimal, imposing significant overheads in terms of computation...

A t-out-of-n threshold ring signature allows $t$ parties to jointly sign a message on behalf of $n$ parties without revealing the identities of the signers. In this paper, we introduce a new generic construction for threshold ring signature, called GCTRS, which can be built on top of a selection on identification schemes, commitment schemes and a new primitive called t-out-of-n proof protocol which is a special type of zero-knowledge proof. In general, our design enables a group of $t$...

In digital advertising, accurate measurement is essential for optimiz- ing ad performance, requiring collaboration between advertisers and publishers to compute aggregate statistics—such as total conver- sions—while preserving user privacy. Traditional secure two-party computation methods allow joint computation on single-identifier data without revealing raw inputs, but they fall short when mul- tidimensional matching is needed and leak the intersection size, exposing sensitive information...

Decentralized e-voting enables secure and transparent elections without relying on trusted authorities, with blockchain emerging as a popular platform. It has compelling applications in Decentralized Autonomous Organizations (DAOs), where governance relies on voting with blockchain-issued tokens. Quadratic voting (QV), a mechanism that mitigates the dominance of large token holders, has been adopted by many DAO elections to enhance fairness. However, current QV systems deployed in practice...

In many fields, the need to securely collect and aggregate data from distributed systems is growing. However, designs that rely solely on encrypted data transmission make it difficult to trace malicious users. To address this challenge, we have enhanced the secure aggregation (SA) protocol proposed by Bell et al. (CCS 2020) by introducing verification features that ensure compliance with user inputs and encryption processes while preserving data privacy. We present LZKSA, a quantum-safe...

The Internet of Things (IoT) has become integral to modern life, enabling smart cities, healthcare, and industrial automation. However, the increasing connectivity of IoT devices exposes them to various cyber threats, necessitating robust encryption methods. The PRESENT cipher, a lightweight block cipher, is well-suited for resource-constrained IoT environments, offering strong security with minimal computational overhead. This paper explores the application of deep learning (DL) techniques...

In the Social Internet of Vehicles (SIoV), effective data sharing is essential for applications including road safety, traffic management, and situational awareness. However, the decentralized and open nature of SIoV presents significant challenges in simultaneously ensuring data integrity, user privacy, and system accountability. This paper presents a protocol for secure and location-accurate traffic data sharing that fully preserves the anonymity and privacy of participating witnesses. The...

Differentially private stochastic gradient descent (DP-SGD) trains machine learning (ML) models with formal privacy guarantees for the training set by adding random noise to gradient updates. In collaborative learning (CL), where multiple parties jointly train a model, noise addition occurs either (i) before or (ii) during secure gradient aggregation. The first option is deployed in distributed DP methods, which require greater amounts of total noise to achieve security, resulting in...

We initiate the study of a new abstraction called incremental decentralized data archival (${\sf iDDA}$). Specifically, imagine that there is an ever-growing, massive database such as a blockchain, a comprehensive human knowledge base like Wikipedia, or the Internet archive. We want to build a decentralized archival of such datasets to ensure long-term robustness and sustainability. We identify several important properties that an ${\sf iDDA}$ scheme should satisfy. First,...

Registered functional encryption (RFE) is a generalization of public-key encryption that enables computation on encrypted data (like classical FE), but without needing a central trusted authority. Concretely, the users choose their own public keys and register their keys together with a function with an (untrusted) key curator. The key curator aggregates all of the individual public keys into a short master public key, which serves as the public key of the FE scheme. Currently, we only...

We show that the certificateless signature scheme [Veh. Commun. 47: 100763 (2024)] is insecure, because an adversary can launch forgery attack for any message. The signer's certificateless public key is not tightly bound to the system public key. The inherent flaw results in that the adversary can find an efficient signing algorithm functionally equivalent to the valid signing algorithm. The findings in this note could be helpful for newcomers who are not familiar with the designing...

The security of block ciphers such as AES-128, AES-192, and AES-256 relies on the assumption that their ciphertext outputs are computationally indistinguishable from random permutations. While distinguishers have been proposed for reduced-round variants or under non-standard models such as known-key or chosen-key settings, no effective distinguisher has been demonstrated for the full-round AES ciphers in the standard secret-key model. This work introduces FESLA (Feature Enhanced...

As a theoretical cornerstone of post-quantum cryptography, the Learning With Errors (LWE) problem serves as the security foundation for standardized algorithms such as Kyber and Dilithium. Recently, a framework for provable dual attacks on LWE has been proposed by Pouly et al. in Eurocrypt 2024, addressing the limitations in effectiveness caused by existing methods' reliance on heuristic assumptions in LWE dual attacks. Their paper also poses an open problem on how to formally integrate...