The SPDZ protocol family is a popular choice for secure multi-party computation (MPC) in a dishonest majority setting with active adversaries. Over the past decade, a series of studies have focused on improving its offline phase, where special additive shares, called authenticated triples, are generated. However, to accommodate recent demands for matrix operations in secure machine learning and big integer arithmetic in distributed RSA key generation, updates to the offline phase are...

Homomorphic message authenticators allow a user to perform computation on previously authenticated data producing a tag $\sigma$ that can be used to verify the authenticity of the computation. We extend this notion to consider a multi-party setting where we wish to produce a tag that allows verifying (possibly different) computations on all party's data at once. Moreover, the size of this tag should not grow as a function of the number of parties or the complexity of the computations. We...

Distributed training that enables multiple parties to jointly train a model on their respective datasets is a promising approach to address the challenges of large volumes of diverse data for training modern machine learning models. However, this approach immedi- ately raises security and privacy concerns; both about each party wishing to protect its data from other parties during training and preventing leakage of private information from the model after training through various...

Many of the currently best actively secure Multi-Party Computation (MPC) protocols like SPDZ (Damgård et al., CRYPTO 2012) and improvements thereof use correlated randomness to speed up the time-critical online phase. Although many of these protocols still rely on classical Beaver triples, recent results show that more complex correlations like matrix or convolution triples lead to more efficient evaluations of the corresponding operations, i.e. matrix multiplications or tensor convolutions....

Secure Multi-party Computation (MPC) provides a promising solution for privacy-preserving multi-source data analytics. However, existing MPC-based collaborative analytics systems (MCASs) have unsatisfying performance for scenarios with dynamic databases. Naively running an MCAS on a dynamic database would lead to significant redundant costs and raise performance concerns, due to the substantial duplicate contents between the pre-updating and post-updating databases. In this paper, we...

Federated Learning (FL) enables multiple clients to collaboratively train a machine learning model while keeping their data private, eliminating the need for data sharing. Two common approaches to secure aggregation (SA) in FL are the single-aggregator and multiple-aggregator models. Existing multiple-aggregator protocols such as Prio (NSDI 2017), Prio+ (SCN 2022), Elsa (S\&P 2023) either offer robustness only in the presence of semi-honest servers or provide security without...

We present novel Secure Multi-Party Computation (SMPC) protocols to perform Breadth-First-Searches (BFSs) and determine maximal flows on dense secret-shared graphs. In particular, we introduce a novel BFS protocol that requires only $\mathcal{O}(\log n)$ communication rounds on graphs with $n$ nodes, which is a big step from prior work that requires $\mathcal{O}(n \log n)$ rounds. This BFS protocol is then used in a maximal flow protocol based on the Edmonds-Karp algorithm, which requires...

Secure multi-party computation (MPC) enables multiple distrusting parties to jointly compute a function while keeping their inputs private. Computing the AES block cipher in MPC, where the key and/or the input are secret-shared among the parties is important for various applications, particularly threshold cryptography. In this work, we propose a family of dedicated, high-performance MPC protocols to compute the non-linear S-box part of AES in the honest majority setting. Our protocols...

We consider protocols for secure multi-party computation (MPC) built from FHE under honest majority, i.e., for $n=2t+1$ players of which $t$ are corrupt, that are robust. Surprisingly there exists no robust threshold FHE scheme based on BFV to design such MPC protocols. Precisely, all existing methods for generating a common relinearization key can abort as soon as one player deviates. We address this issue, with a new relinearization key (adapted from [CDKS19, CCS'19]) which we show how to...

For secure multi-party computation in the line of the secret-sharing based SPDZ protocol, actively secure multiplications consume correlated randomness in the form of authenticated Beaver triples, which need to be generated in advance. Although it is a well-studied problem, the generation of Beaver triples is still a bottleneck in practice. In the two-party setting, the best solution with low communication overhead is the protocol by Boyle et al. (Crypto 2020), which is derived from...

Privacy-preserving machine learning (PPML) enables multiple distrusting parties to jointly train ML models on their private data without revealing any information beyond the final trained models. In this work, we study the client-aided two-server setting where two non-colluding servers jointly train an ML model on the data held by a large number of clients. By involving the clients in the training process, we develop efficient protocols for training algorithms including linear regression,...

Multi-party private set union (MPSU) protocol enables $m$ $(m > 2)$ parties, each holding a set, to collectively compute the union of their sets without revealing any additional information to other parties. There are two main categories of multi-party private set union (MPSU) protocols: The first category builds on public-key techniques, where existing works require a super-linear number of public-key operations, resulting in their poor practical efficiency. The second category builds on...

Oblivious transfer (OT) is a fundamental cryptographic protocol that plays a crucial role in secure multi-party computation (MPC). Most practical OT protocols by, e.g., Naor and Pinkas (SODA'01) or Chou and Orlandi (Latincrypt'15), are based on Diffie-Hellman (DH)-like assumptions and not post-quantum secure. In contrast, many other components of MPC protocols, including garbled circuits and secret sharings, are post-quantum secure. The reliance on non-post-quantum OT protocols presents a...

Online ride-sharing services (RSS) have become very popular owing to increased awareness of environmental concerns and as a response to increased traffic congestion. To request a ride, users submit their locations and route information for ride matching to a service provider (SP), leading to possible privacy concerns caused by leakage of users' location data. We propose QuickPool, an efficient SP-aided RSS solution that can obliviously match multiple riders and drivers simultaneously,...

Oblivious Transfer (OT) is at the heart of secure computation and is a foundation for many applications in cryptography. Over two decades of work have led to extremely efficient protocols for evaluating OT instances in the preprocessing model, through a paradigm called OT extension. A few OT instances generated in an offline phase can be used to perform many OTs in an online phase efficiently, i.e., with very low communication and computational overheads. Specifically, traditional OT...

Decision trees are an important class of supervised learning algorithms. When multiple entities contribute data to train a decision tree (e.g. for fraud detection in the financial sector), data privacy concerns necessitate the use of a privacy-enhancing technology such as secure multi-party computation (MPC) in order to secure the underlying training data. Prior state-of-the-art (Hamada et al.) construct an MPC protocol for decision tree training with a communication of $\mathcal{O}(hmN\log...

Researchers across various fields seek to understand causal relationships but often find controlled experiments impractical. To address this, statistical tools for causal discovery from naturally observed data have become crucial. Non-linear regression models, such as Gaussian process regression, are commonly used in causal inference but have limitations due to high costs when adapted for secure computation. Support vector regression (SVR) offers an alternative but remains costly in an...

We develop a distributed service for generating correlated randomness (e.g. permutations) for multiple parties, where each party’s output is private but publicly verifiable. This service provides users with a low-cost way to play online poker in real-time, without a trusted party. Our service is backed by a committee of compute providers, who run a multi-party computation (MPC) protocol to produce an (identity-based) encrypted permutation of a deck of cards, in an offline phase well ahead...

Machine learning is widely used for a range of applications and is increasingly offered as a service by major technology companies. However, the required massive data collection raises privacy concerns during both training and inference. Privacy-preserving machine learning aims to solve this problem. In this setting, a collection of servers secret share their data and use secure multi-party computation to train and evaluate models on the joint data. All prior work focused on the scenario...

We present a new simulation-secure quantum oblivious transfer (QOT) protocol based on one-way functions in the plain model. With a focus on practical implementation, our protocol surpasses prior works in efficiency, promising feasible experimental realization. We address potential experimental errors and their correction, offering analytical expressions to facilitate the analysis of the required quantum resources. Technically, we achieve simulation security for QOT through an equivocal and...

An oblivious pseudorandom function (OPRF) is a two-party protocol in which a party holds an input and the other party holds the PRF key, such that the party having the input only learns the PRF output and the party having the key would not learn the input. Now, in a threshold oblivious pseudorandom function (TOPRF) protocol, a PRF key K is initially shared among T servers. A client can obtain a PRF value by interacting with t(≤ T) servers but is unable to compute the same with up to (t − 1)...

Secure multi-party computation (MPC) in a three-party, honest majority scenario is currently the state-of-the-art for running machine learning algorithms in a privacy-preserving manner. For efficiency reasons, fixed-point arithmetic is widely used to approximate computation over decimal numbers. After multiplication in fixed-point arithmetic, truncation is required to keep the result's precision. In this paper, we present an efficient three-party truncation protocol secure in the presence of...

Oblivious Transfer (OT) is a fundamental cryptographic protocol with applications in secure Multi-Party Computation, Federated Learning, and Private Set Intersection. With the advent of quantum computing, it is crucial to develop unconditionally secure core primitives like OT to ensure their continued security in the post-quantum era. Despite over four decades since OT's introduction, the literature has predominantly relied on computational assumptions, except in cases using unconventional...

Neural network inference as a service enables a cloud server to provide inference services to clients. To ensure the privacy of both the cloud server's model and the client's data, secure neural network inference is essential. Binarized neural networks (BNNs), which use binary weights and activations, are often employed to accelerate inference. However, achieving secure BNN inference with secure multi-party computation (MPC) is challenging because MPC protocols cannot directly operate on...

Collaborative graph learning represents a learning paradigm where multiple parties jointly train a graph neural network (GNN) using their own proprietary graph data. To honor the data privacy of all parties, existing solutions for collaborative graph learning are either based on federated learning (FL) or secure machine learning (SML). Although promising in terms of efficiency and scalability due to their distributed training scheme, FL-based approaches fall short in providing provable...

The parallel broadcast (PBC) problem generalises the classic Byzantine broadcast problem to the setting where all $n$ nodes broadcast a message and deliver $O(n)$ messages. PBC arises naturally in many settings including multi-party computation. Recently, Tsimos, Loss, and Papamanthou (CRYPTO 2022) showed PBC protocols with improved communication, against an adaptive adversary who can corrupt all but a constant fraction $\epsilon$ of nodes (i.e., $f < (1 - \epsilon)n$). However, their study...

The proliferation of artificial intelligence and big data has resulted in a surge in data demand and increased data dimensionality. This escalation has consequently heightened the costs associated with storage and processing. Concurrently, the confidential nature of data collected by various institutions, which cannot be disclosed due to personal privacy concerns, has exacerbated the challenges associated with data analysis and machine learning model training. Therefore, designing a secure...

This work introduces homomorphic secret sharing (HSS) with succinct share size. In HSS, private inputs are shared between parties, who can then homomorphically evaluate a function on their shares, obtaining a share of the function output. In succinct HSS, a portion of the inputs can be distributed using shares whose size is sublinear in the number of such inputs. The parties can then locally evaluate a function $f$ on the shares, with the restriction that $f$ must be linear in the succinctly...

Private information retrieval (PIR) is a fundamental cryptographic primitive that allows a user to fetch a database entry without revealing to the server which database entry it learns. PIR becomes non-trivial if the server communication is less than the database size. We show that building (even) very weak forms of single-server PIR protocols, without pre-processing, requires the number of public-key operations to scale linearly in the database size. This holds irrespective of the number of...

Time-lock puzzles are unique cryptographic primitives that use computational complexity to keep information secret for some period of time, after which security expires. Unfortunately, current analysis techniques of time-lock primitives provide no sound mechanism to build multi-party cryptographic protocols which use expiring security as a building block. We explain in this paper that all other attempts at this subtle problem lack either composability, a fully consistent analysis, or...

Topic modelling refers to a popular set of techniques used to discover hidden topics that occur in a collection of documents. These topics can, for example, be used to categorize documents or label text for further processing. One popular topic modelling technique is Latent Dirichlet Allocation (LDA). In topic modelling scenarios, the documents are often assumed to be in one, centralized dataset. However, sometimes documents are held by different parties, and contain privacy- or...

We revisit the alternating-moduli paradigm for constructing symmetric-key primitives with a focus on constructing efficient protocols to evaluate them using secure multi-party computation (MPC). The alternating-moduli paradigm of Boneh, Ishai, Passelègue, Sahai, and Wu (TCC 2018) enables the construction of various symmetric-key primitives with the common characteristic that the inputs are multiplied by two linear maps over different moduli. The first contribution focuses on...

Despite much progress, general-purpose secure multi-party computation (MPC) with active security may still be prohibitively expensive in settings with large input datasets. This particularly applies to the secure evaluation of graph algorithms, where each party holds a subset of a large graph. Recently, Araki et al. (ACM CCS '21) showed that dedicated solutions may provide significantly better efficiency if the input graph is sparse. In particular, they provide an efficient protocol for...

The Gradient Boosting Decision Tree (GBDT) is a well-known machine learning algorithm, which achieves high performance and outstanding interpretability in real-world scenes such as fraud detection, online marketing and risk management. Meanwhile, two data owners can jointly train a GBDT model without disclosing their private dataset by executing secure Multi-Party Computation (MPC) protocols. In this work, we propose NodeGuard, a highly efficient two party computation (2PC) framework for...

Biometric authentication eliminates the need for users to remember secrets and serves as a convenient mechanism for user authentication. Traditional implementations of biometric-based authentication store sensitive user biometry on the server and the server becomes an attractive target of attack and a source of large-scale unintended disclosure of biometric data. To mitigate the problem, we can resort to privacy-preserving computation and store only protected biometrics on the server. While...

We present new secure multi-party computation protocols for linear algebra over a finite field, which improve the state-of-the-art in terms of security. We look at the case of \emph{unconditional security with perfect correctness}, i.e., information-theoretic security without errors. We notably propose an expected constant-round protocol for solving systems of $m$ linear equations in $n$ variables over $\mathbb{F}_q$ with expected complexity $O(k(n^{2.5} + m^{2.5}+n^2m^{0.5}))$ where $k >...

Nowadays Federated learning (FL) is established as one of the best techniques for collaborative machine learning. It allows a set of clients to train a common model without disclosing their sensitive and private dataset to a coordination server. The latter is in charge of the model aggregation. However, FL faces some problems, regarding the security of updates, integrity of computation and the availability of a server. In this paper, we combine some new ideas like clients’ reputation with...

In the common random string model, the parties executing a protocol have access to a uniformly random bit string. It is known that under standard intractability assumptions, we can realize any ideal functionality with universally composable (UC) security if a trusted common random string (CrS) setup is available. It was always a question of where this CrS should come from since the parties provably could not compute it themselves. Trust assumptions are required, so minimizing the level of...

Secure multi-party computation aims to allow a set of players to compute a given function on their secret inputs without revealing any other information than the result of the computation. In this work, we focus on the design of secure multi-party protocols for shared polynomial operations. We consider the classical model where the adversary is honest-but-curious, and where the coefficients (or any secret values) are either encrypted using an additively homomorphic encryption scheme or...

An Oblivious Pseudo-Random Function (OPRF) is a two-party protocol for jointly evaluating a Pseudo-Random Function (PRF), where a user has an input x and a server has an input k. At the end of the protocol, the user learns the evaluation of the PRF using key k at the value x, while the server learns nothing about the user's input or output. OPRFs are a prime tool for building secure authentication and key exchange from passwords, private set intersection, private information retrieval,...

Secure Multi-party Computation (MPC) allows two or more parties to compute any public function over their privately-held inputs, without revealing any information beyond the result of the computation. Modern protocols for MPC generate a large amount of input-independent preprocessing material called multiplication triples, in an offline phase. This preprocessing can later be used by the parties to efficiently instantiate an input-dependent online phase computing the function. To date, the...

EdDSA, standardized by both IRTF and NIST, is a variant of the well-known Schnorr signature scheme based on Edwards curves, benefitting from stateless and deterministic derivation of nonces (i.e., it does not require a reliable source of randomness or state continuity). Recently, NIST called for multi-party threshold EdDSA signatures in one mode of verifying such nonce derivation via zero-knowledge (ZK) proofs. However, it is challenging to translate the stateless and deterministic benefits...

In this work, we focus on Single-Input Functionality (SIF), which can be viewed as a special case of MPC. In a SIF, only one distinguished party called the dealer holds a private input. SIF allows the dealer to perform a computation task with other parties without revealing any additional information about the private input. SIF has diverse applications, including multiple-verifier zero-knowledge, and verifiable relation sharing. As our main contribution, we propose the first 1-round SIF...

Secure multi-party computation (MPC) allows a set of $n$ parties to jointly compute a function over their private inputs. The seminal works of Ben-Or, Canetti and Goldreich [STOC '93] and Ben-Or, Kelmer and Rabin [PODC '94] settled the feasibility of MPC over asynchronous networks. Despite the significant line of work devoted to improving the communication complexity, current protocols with information-theoretic security and optimal resilience $t<n/3$ communicate $\Omega(n^4C)$ field...

Insight into user experience and behavior is critical to the success of large software systems and web services. Gaining such insights, while preserving user privacy, is a significant challenge. Recent advancements in multi-party computation have made it practical to securely compute aggregates over secret shared data. Two such protocols have emerged as candidates for standardization at the IETF: Prio (NSDI 2017) for general-purpose statistics; and Poplar (IEEE S&P 2021) for heavy hitters,...

The recent surge of distribute technologies caused an increasing interest towards threshold signature protocols, that peaked with the recent NIST First Call for Multi-Party Threshold Schemes. Since its introduction, the Fiat-Shamir Transform has been the most popular way to design standard digital signature schemes. Many threshold signature schemes are designed in a way that recalls the structure of digital signatures created using Fiat Shamir, by having the signers generate a common...

Typical results in multi-party computation (in short, MPC) capture faulty parties by assuming a threshold adversary corrupting parties actively and/or fail-corrupting. These corruption types are, however, inadequate for capturing correct parties that might suffer temporary network failures and/or localized faults - these are particularly relevant for MPC over large, global scale networks. Omission faults and general adversary structures have been proposed as more suitable alternatives....

The notion of collaborative zk-SNARK is introduced by Ozdemir and Boneh (USENIX 2022), which allows multiple parties to jointly create a zk-SNARK proof over distributed secrets (also known as the witness). This approach ensures the privacy of the witness, as no corrupted servers involved in the proof generation can learn anything about the honest servers' witness. Later, Garg et al. continued the study, focusing on how to achieve faster proof generation (USENIX 2023). However, their...

In recent breakthrough results, novel use of garbled circuits yielded constructions for several primitives like Identity-Based Encryption (IBE) and 2-round secure multi-party computation, based on standard assumptions in public-key cryptography. While the techniques in these different results have many common elements, these works did not offer a modular abstraction that could be used across them. Our main contribution is to introduce a novel notion of obfuscation, called Reach-Restricted...

In a seminal work, Ishai et al. (FOCS–2006) studied the viability of designing unconditionally secure protocols for key agreement and secure multi-party computation (MPC) using an anonymous bulletin board (ABB) as a building block. While their results establish the feasibility of key agreement and honest-majority MPC in the ABB model, the optimality of protocols with respect to their round and communication complexity is not studied. This paper enriches this study of unconditional security...

This work presents the first hardware realisation of the Syndrome-Decoding-in-the-Head (SDitH) signature scheme, which is a candidate in the NIST PQC process for standardising post-quantum secure digital signature schemes. SDitH's hardness is based on conservative code-based assumptions, and it uses the Multi-Party-Computation-in-the-Head (MPCitH) construction. This is the first hardware design of a code-based signature scheme based on traditional decoding problems and only the second for...

Money laundering is a serious financial crime where criminals aim to conceal the illegal source of their money via a series of transactions. Although banks have an obligation to monitor transactions, it is difficult to track these illicit money flows since they typically span over multiple banks, which cannot share this information due to privacy concerns. We present secure risk propagation, a novel efficient algorithm for money laundering detection across banks without violating privacy...

Secure Multi-Party Computation (MPC) constructions typically allow computation over a finite field or ring. While useful for many applications, certain real-world applications require the usage of decimal numbers. While it is possible to emulate floating-point operations in MPC, fixed-point computation has gained more traction in the practical space due to its simplicity and efficient realizations. Even so, current protocols for fixed-point MPC still require computing a secure truncation...

We explore the issue of anonymously proving account ownership (anonymous PAO). Such proofs allow a prover to prove to a verifier that it owns a valid account at a server without being tracked by the server or the verifier, without requiring any changes at the server's end and without even revealing to it that any anonymous PAO is taking place. This concept is useful in sensitive applications like whistleblowing. The first introduction of anonymous PAOs was by Wang et al., who also introduced...

Through tremendous efforts, the communication cost of secure multi-party computation (MPC) in the honest-majority setting has been significantly improved. In particular, the state-of-the-art honest-majority MPC protocol by Escudero et al. (CCS'22) takes 12 field elements in total per multiplication gate for arithmetic circuits in the online phase. However, it still requires $12 \log(5n/4)$ bits of online communication per AND gate for Boolean circuits. That is, for Boolean circuits, no...

We study a fundamental problem in Multi-Party Computation, which we call the Multiple Millionaires’ Problem (MMP). Given a set of private integer inputs, the problem is to identify the subset of inputs that equal the maximum (or minimum) of that set, without revealing any further information on the inputs beyond what is implied by the desired output. Such a problem is a natural extension of the Millionaires’ Problem, which is the very first Multi- Party Computation problem that was...

In 1982, Yao introduced the problem of comparing two private values, thereby launching the study of protocols for secure multi-party computation (MPC). Since then, comparison protocols have undergone extensive study and found widespread applications. We survey state-of-the-art comparison protocols for an arbitrary number of parties, decompose them into smaller primitives and analyse their communication complexity under the usual assumption that the underlying MPC protocol does...

This paper studies a multi-party private set union (mPSU), a fundamental cryptographic problem that allows multiple parties to compute the union of their respective datasets without revealing any additional information. We propose an efficient mPSU protocol which is secure in the presence of any number of colluding semi-honest participants. Our protocol avoids computationally expensive homomorphic operations or generic multi-party computation, thus providing an efficient solution for mPSU....

In this work, we introduce FANNG-MPC, a versatile secure multi-party computation framework capable to offer active security for privacy preserving machine learning as a service (MLaaS). Derived from the now deprecated SCALE-MAMBA, FANNG is a data-oriented fork, featuring novel set of libraries and instructions for realizing private neural networks, effectively reviving the popular framework. To the best of our knowledge, FANNG is the first MPC framework to offer actively secure MLaaS in the...

We consider how to design an anonymous data collection protocol that enforces compliance rules. Imagine that each client contributes multiple data items (e.g., votes, location crumbs, or secret shares of its input) to an anonymous network, which mixes all clients' data items so that the receiver cannot determine which data items belong to the same user. Now, each user must prove to an auditor that the set it contributed satisfies a compliance predicate, without identifying which items it...

We present Private Random Access Computations (PRAC), a 3-party Secure Multi-Party Computation (MPC) framework to support random-access data structure algorithms for MPC with efficient communication in terms of rounds and bandwidth. PRAC extends the state-of-the-art DORAM Duoram with a new implementation, more flexibility in how the DORAM memory is shared, and support for Incremental and Wide DPFs. We then use these DPF extensions to achieve algorithmic improvements in three novel...

The advent of transformers has brought about significant advancements in traditional machine learning tasks. However, their pervasive deployment has raised concerns about the potential leakage of sensitive information during inference. Existing approaches using secure multiparty computation (MPC) face limitations when applied to transformers due to the extensive model size and resource-intensive matrix-matrix multiplications. In this paper, we present BOLT, a privacy-preserving inference...

In recent years, symmetric primitives that focus on arithmetic metrics over large finite fields, characterized as arithmetization-oriented (\texttt{AO}) ciphers, are widely used in advanced protocols such as secure multi-party computations (MPC), fully homomorphic encryption (FHE) and zero-knowledge proof systems (ZK). To ensure good performance in protocols, these \texttt{AO} ciphers are commonly designed with a small number of multiplications over finite fields and low multiplicative...

Bulletin boards (BB) are important cryptographic building blocks that, at their core, provide a broadcast channel with memory. BBs are widely used within many security protocols, including secure multi-party computation protocols, e-voting systems, and electronic auctions. Even though the security of protocols crucially depends on the underlying BB, as also highlighted by recent works, the literature on constructing secure BBs is sparse. The so-far only provably secure BBs require trusted...

Over years of the development of secure multi-party computation (MPC), many sophisticated functionalities have been made pratical and multi-dimensional operations occur more and more frequently in MPC protocols, especially in protocols involving datasets of vector elements, such as privacy-preserving biometric identification and privacy-preserving machine learning. In this paper, we introduce a new kind of correlation, called tensor triples, which is designed to make multi-dimensional MPC...

We show the following unconditional results on quantum commitments in two related yet different models: 1. We revisit the notion of quantum auxiliary-input commitments introduced by Chailloux, Kerenidis, and Rosgen (Comput. Complex. 2016) where both the committer and receiver take the same quantum state, which is determined by the security parameter, as quantum auxiliary inputs. We show that computationally-hiding and statistically-binding quantum auxiliary-input commitments exist...

In their seminal work, Ishai, Kushilevitz, Ostrovsky, and Sahai (STOC`07) presented the MPC-in-the-Head paradigm, which shows how to design Zero-Knowledge Proofs (ZKPs) from secure Multi-Party Computation (MPC) protocols. This paradigm has since then revolutionized and modularized the design of efficient ZKP systems, with far-reaching applications beyond ZKPs. However, to the best of our knowledge, all previous instantiations relied on fully-secure MPC protocols, and have not been able to...

Secure computation enables mutually distrusting parties to jointly compute a function on their secret inputs, while revealing nothing beyond the function output. A long-running challenge is understanding the required communication complexity of such protocols---in particular, when communication can be sublinear in the circuit representation size of the desired function. Significant advances have been made affirmatively answering this question within the two-party setting, based on a...

The classic stable matching algorithm of Gale and Shapley (American Mathematical Monthly '69) and subsequent variants such as those by Roth (Mathematics of Operations Research '82) and Abdulkadiroglu et al. (American Economic Review '05) have been used successfully in a number of real-world scenarios, including the assignment of medical-school graduates to residency programs, New York City teenagers to high schools, and Norwegian and Singaporean students to schools and universities. However,...

Secure multi-party computation (MPC) enables (joint) computations on sensitive data while maintaining privacy. In real-world scenarios, asymmetric trust assumptions are often most realistic, where one somewhat trustworthy entity interacts with smaller clients. We generalize previous two-party computation (2PC) protocols like MUSE (USENIX Security'21) and SIMC (USENIX Security'22) to the three-party setting (3PC) with one malicious party, avoiding the performance limitations of...

Among secure multi-party computation protocols, linear secret sharing schemes often do not rely on cryptographic assumptions and are among the most straightforward to explain and to implement correctly in software. However, basic versions of such schemes either limit participants to evaluating linear operations involving private values or require those participants to communicate synchronously during a computation phase. A straightforward, information-theoretically secure extension to such...

Protocols for secure multi-party computation (MPC) supporting mixed-mode computation have found a lot of applications in recent years due to their flexibility in representing the function to be evaluated. However, existing mixed-mode MPC protocols are only practical for a small number of parties: they are either tailored to the case of two/three parties, or scale poorly for a large number of parties. In this paper, we design and implement a new system for highly efficient and scalable...

Many secure computation schemes and protocols (such as numerous variants of secure multi-party computation and homomorphic encryption) have favorable performance characteristics when they are used to evaluate addition and scalar multiplication operations on private values that can be represented as ring elements. A purely algebraic argument (with no references to any specific protocol or scheme) can be used to show that the ability to perform these operations is sufficient to implement any...

Domain Generation Algorithms (DGAs) are used by malware to generate pseudorandom domain names to establish communication between infected bots and Command and Control servers. While DGAs can be detected by machine learning (ML) models with great accuracy, offering DGA detection as a service raises privacy concerns when requiring network administrators to disclose their DNS traffic to the service provider. We propose the first end-to-end framework for privacy-preserving classification as a...

One-way functions are central to classical cryptography. They are both necessary for the existence of non-trivial classical cryptosystems, and sufficient to realize meaningful primitives including commitments, pseudorandom generators and digital signatures. At the same time, a mounting body of evidence suggests that assumptions even weaker than one-way functions may suffice for many cryptographic tasks of interest in a quantum world, including bit commitments and secure multi-party...

While secure multi-party computation (MPC) protects the privacy of inputs and intermediate values of a computation, differential privacy (DP) ensures that the output itself does not reveal too much about individual inputs. For this purpose, MPC can be used to generate noise and add this noise to the output. However, securely generating and adding this noise is a challenge considering real-world implementations on finite-precision computers, since many DP mechanisms guarantee privacy only...

Over the past few years, homomorphic secret sharing (HSS) emerged as a compelling alternative to fully homomorphic encryption (FHE), due to its feasibility from an array of standard assumptions and its potential efficiency benefits. However, all known HSS schemes, with the exception of schemes built from FHE or indistinguishability obfuscation (iO), can only support two or four parties. In this work, we give the first construction of a multi-party HSS scheme for a non-trivial function...

The MPC-in-the-Head paradigm is instrumental in building zero-knowledge proof systems and post-quantum signatures using techniques from secure multi-party computation. In this work, we extend and improve the recently proposed framework of MPC-in-the-Head based on threshold secret sharing, here called Threshold Computation in the Head. We first address some limitations of this framework, namely its overhead in the communication cost, its constraint on the number of parties and its degradation...

Cheater identification in secure multi-party computation (MPC) allows the honest parties to agree upon the identity of a cheating party, in case the protocol aborts. In the context of a dishonest majority, this becomes especially critical, as it serves to thwart denial-of-service attacks and mitigate known impossibility results on ensuring fairness and guaranteed output delivery. In this work, we present a new, lightweight approach to achieving identifiable abort in dishonest majority...

Garbled circuit techniques that are secure in the adaptive setting -- where inputs are chosen after the garbled program is sent -- are motivated by practice, but they are notoriously difficult to achieve. Prior adaptive garbling is either impractically expensive or encrypts the entire garbled program with the output of a programmable random oracle (PRO), a strong assumption. We present a simple framework for proving adaptive security of garbling schemes in the non-programmable random...

Distributing the Elliptic Curve Digital Signature Algorithm (ECDSA) has received increased attention in past years due to the wide range of applications that can benefit from this, particularly after the popularity that the blockchain technology has gained. Many schemes have been proposed in the literature to improve the efficiency of multi- party ECDSA. Most of these schemes either require heavy homomorphic encryption computation or multiple executions of a functionality...

Secure multi-party computation (MPC) enables multiple distrusting parties to compute a function while keeping their respective inputs private. In a threshold implementation of a symmetric primitive, e.g., of a block cipher, each party holds a share of the secret key or of the input block. The output block is computed without reconstructing the secret key. This enables the construction of distributed TPMs or transciphering for secure data transmission in/out of the MPC context. This paper...

Fuzzy Password-Authenticated Key Exchange (fuzzy PAKE) allows cryptographic keys to be generated from authentication data that is both fuzzy and of low entropy. The strong protection against offline attacks offered by fuzzy PAKE opens an interesting avenue towards secure biometric authentication, typo-tolerant password authentication, and automated IoT device pairing. Previous constructions of fuzzy PAKE are either based on Error Correcting Codes (ECC) or generic multi-party computation...

Multi-party private set union (MPSU) allows \(k(k\geq 3)\) parties, each holding a dataset of known size, to compute the union of their sets without revealing any additional information. Although two-party PSU has made rapid progress in recent years, applying its effective techniques to the multi-party setting would render information leakage and thus cannot be directly extended. Existing MPSU protocols heavily rely on computationally expensive public-key operations or generic secure...

IoT devices collect privacy-sensitive data, e.g., in smart grids or in medical devices, and send this data to cloud servers for further processing. In order to ensure confidentiality as well as authenticity of the sensor data in the untrusted cloud environment, we consider a transciphering scenario between embedded IoT devices and multiple cloud servers that perform secure multi-party computation (MPC). Concretely, the IoT devices encrypt their data with a lightweight symmetric cipher and...

Emails have improved our workplace efficiency and communication. However, they are often processed unencrypted by mail servers, leaving them open to data breaches on a single service provider. Public-key based solutions for end-to-end secured email, such as Pretty Good Privacy (PGP) and Secure/Multipurpose Internet Mail Extensions (S/MIME), are available but are not widely adopted due to usability obstacles and also hinder processing of encrypted emails. We propose PrivMail, a novel...

Keller and Sun (ICML'22) have found a gap in the accuracy between floating-point deep learning in cleartext and secure quantized deep learning using multi-party computation. We have discovered that this gap is caused by a bug in the implementation of max-pooling. In this note, we present updated figures to support this conclusion. We also add figures for another network on CIFAR-10.

Recently, Abdalla, Gong and Wee (Crypto 2020) provided the first functional encryption scheme for attribute-weighted sums (AWS), where encryption takes as input $N$ (unbounded) attribute-value pairs $\{\vec{x}_i, \vec{z}_i\}_{I \in [N]}$ where $\vec{x}_i$ is public and $\vec{z}_i$ is private, the secret key is associated with an arithmetic branching programs $f$, and decryption returns the weighted sum ${\sum}_{{i \in [N]}} f(\vec{x}_i)^\top \vec{z}_i$, leaking no additional information...

We present new protocols for threshold Schnorr signatures that work in an asynchronous communication setting, providing robustness and optimal resilience. These protocols provide unprecedented performance in terms of communication and computational complexity. In terms of communication complexity, for each signature, a single party must transmit a few dozen group elements and scalars across the network (independent of the size of the signing committee). In terms of computational...

In oblivious finite automata evaluation, one party holds a private automaton, and the other party holds a private string of characters. The objective is to let the parties know whether the string is accepted by the automaton or not, while keeping their inputs secret. The applications include DNA searching, pattern matching, and more. Most of the previous works are based on asymmetric cryptographic primitives, such as homomorphic encryption and oblivious transfer. These primitives are...

Secret sharing is a cryptographic primitive that divides a secret into several shares, and allows only some combinations of shares to recover the secret. As it can also be used in secure multi-party computation protocol with outsourcing servers, several variations of secret sharing are devised for this purpose. Most of the existing protocols require the number of computing servers to be determined in advance. However, in some situations we may want the system to be "evolving". We may want to...

We present TVA, a multi-party computation (MPC) system for secure analytics on secret-shared time series data. TVA achieves strong security guarantees in the semi-honest and malicious settings, and high expressivity by enabling complex analytics on inputs with unordered and irregular timestamps. TVA is the first system to support arbitrary composition of oblivious window operators, keyed aggregations, and multiple filter predicates, while keeping all data attributes private, including record...

Commodity encrypted storage platforms (e.g., IceDrive, pCloud) permit data store and sharing across multiple users while preserving data confidentiality. However, end-to-end encryption may not be sufficient since it only offers confidentiality when the data is at rest or in transit. Meanwhile, sensitive information can be leaked from metadata representing activities during data operations (e.g., query, processing). Recent encrypted search platforms such as DORY (OSDI’20) or DURASIFT...

Secure multiparty computation$~$(MPC) enables privacy-preserving collaborative computation over sensitive data held by multiple mutually distrusting parties. Unfortunately, in the most natural setting where a majority of the parties are maliciously corrupt$~$(also called the $\textit{dishonest majority}$ setting), traditional MPC protocols incur high overheads and offer weaker security guarantees than are desirable for practical applications. In this paper, we explore the possibility of...

This paper introduces arithmetic sketching, an abstraction of a primitive that several previous works use to achieve lightweight, low-communication zero-knowledge verification of secret-shared vectors. An arithmetic sketching scheme for a language $\mathcal{L} \in \mathbb{F}^n$ consists of (1) a randomized linear function compressing a long input x to a short “sketch,” and (2) a small arithmetic circuit that accepts the sketch if and only if $x \in \mathcal{L}$, up to some small error. If...

It is well known that without randomization, Byzantine agreement (BA) requires a linear number of rounds in the synchronous setting, while it is flat out impossible in the asynchronous setting. The primitive which allows to bypass the above limitation is known as oblivious common coin (OCC). It allows parties to agree with constant probability on a random coin, where agreement is oblivious, i.e., players are not aware whether or not agreement has been achieved. The starting point of our...

We present a reactive MPC protocol built from FHE which is robust in the presence of active adversaries. In addition the protocol enables reduced bandwidth via means of transciphering, and also enables more expressive/efficient programs via means of a $\mathsf{Declassify}$ operation. All sub-components of the protocol can be efficiently realised using existing technology. We prove our protocol secure in the UC framework.

In the classical setting of differential privacy, a privacy-preserving query is performed on a private database, after which the query result is released to the analyst; a differentially private query ensures that the presence of a single database entry is protected from the analyst’s view. In this work, we contribute the first definitional framework for differential privacy in the trusted curator setting; clients submit private inputs to the trusted curator, which then computes individual...

A Single Sign-On (SSO) system allows users to access different remote services while authenticating only once. SSO can greatly improve the usability and security of online activities by dispensing with the need to securely remember or store tens or hundreds of authentication secrets. On the downside, today's SSO providers can track users' online behavior, and collect personal data that service providers want to see asserted before letting a user access their resources. In this work, we...

In recent years, progress in practical applications of secure multi-party computation (MPC), fully homomorphic encryption (FHE), and zero-knowledge proofs (ZK) motivate people to explore symmetric-key cryptographic algorithms, as well as corresponding cryptanalysis techniques (such as differential cryptanalysis, linear cryptanalysis), over general finite fields $\mathbb{F}$ or the additive group induced by $\mathbb{F}^n$. This investigation leads to the break of some MPC/FHE/ZK-friendly...