We present an asynchronous secure multi-party computation (MPC) protocol that is practically efficient. Our protocol can evaluate any arithmetic circuit with linear communication in the number of parties per multiplication gate, while relying solely on computationally lightweight cryptography such as hash function and symmetric encryption. Our protocol is optimally resilient and tolerates $t$ malicious parties among $n = 3t 1$ parties. At the technical level, we manage to apply the...

Asynchronous multiparty computation (AMPC) requires an input agreement phase where all participants have a consistent view of the set of private inputs. While the input agreement problem can be precisely addressed by a Byzantine fault-tolerant consensus known as Asynchronous Common Subset (ACS), existing ACS constructions with potential post-quantum security have a large $\widetilde{\mathcal{O}}(n^3)$ communication complexity for a network of $n$ nodes. This poses a bottleneck for AMPC in...

Transciphering (or Hybrid-Homomorphic Encryption, HHE) is an es- tablished technique for avoiding ciphertext expansion in HE applications, saving communication and storage resources. Recently, it has also been shown to be a fundamental component in the practical construction of HE-based multi-party computation (MPC) protocols, being used both for input data and intermediary results (Smart, IMACC 2023). In these protocols, however, ciphers are used with keys that are jointly generated by...

Reductions are the workhorses of cryptography. They allow constructions of complex cryptographic primitives from simple building blocks. A prominent example is the non-interactive reduction from securely computing a ``complex" function $f$ to securely computing a ``simple" function $g$ via randomized encodings. Prior work equated simplicity with functions of small degree. In this work, we consider a different notion of simplicity where we require $g$ to only take inputs from a small...

With the recent standardization of post-quantum cryptographic algorithms, research efforts have largely remained centered on public key exchange and encryption schemes. Argument systems, which allow a party to efficiently argue the correctness of a computation, have received comparatively little attention regarding their quantum-resilient design. These computational integrity frameworks often rely on cryptographic assumptions, such as pairings or group operations, which are vulnerable to...

This document provides a specification guide for the Multi-party Computation (MPC) setup ceremony for the Tokamak zk-SNARK scheme. It begins by revisiting the MMORPG protocol proposed in BGM17 for Groth16 setup generation, which leverages a random beacon to ensure public randomness. Additionally, it explores the alternative design approach presented in the ``Snarky Ceremonies" paper KMSV21, which removes the need for a random beacon. The document includes a detailed pseudocode and workflow...

The seminal work of Rabin and Ben-Or (STOC'89) showed that the problem of secure $n$-party computation can be solved for $t<n/2$ corruptions with guaranteed output delivery and statistical security. This holds in the traditional static model where the set of parties is fixed throughout the entire protocol execution. The need to better capture the dynamics of large scale and long-lived computations, where compromised parties may recover and the set of parties can change over time, has...

We consider the setting of asynchronous multi-party computation (AMPC) with optimal resilience $n=3t 1$ and linear communication complexity, and employ only ``lightweight'' cryptographic primitives, such as random oracle hash. In this model, we introduce two concretely efficient AMPC protocols for a circuit with $|C|$ multiplication gates: a protocol achieving fairness with $\mathcal{O}(|C|\cdot n n^3)$ field elements of communication, and a protocol achieving guaranteed output delivery...

This paper addresses verifiable consensus of pre-processed circuit polynomials for succinct non-interactive argument of knowledge (SNARK). More specifically, we focus on parts of circuits, referred to as wire maps, which may change based on program inputs or statements being argued. Preparing commitments to wire maps in advance is essential for certain SNARK protocols to maintain their succinctness, but it can be costly. SNARK verifiers can alternatively consider receiving wire maps from an...

Distributed point functions (DPF) are increasingly becoming a foundational tool with applications for application-specific and general secure computation. While two-party DPF constructions are readily available for those applications with satisfiable performance, the three-party ones are left behind in both security and efficiency. In this paper we close this gap and propose the first three-party DPF construction that matches the state-of-the-art two-party DPF on all metrics. Namely, it...

Top trading cycles (TTC) is a famous algorithm for trading indivisible goods between a set of agents such that all agents are as happy as possible about the outcome. In this paper, we present a protocol for executing TTC in a privacy preserving way. To the best of our knowledge, it is the first of its kind. As a technical contribution of independent interest, we suggest a new algorithm for determining all nodes in a functional graph that are on a cycle. The algorithm is particularly well...

Recent advances in differentially private federated learning (DPFL) algorithms have found that using correlated noise across the rounds of federated learning (DP-FTRL) yields provably and empirically better accuracy than using independent noise (DP-SGD). While DP-SGD is well-suited to federated learning with a single untrusted central server using lightweight secure aggregation protocols, secure aggregation is not conducive to implementing modern DP-FTRL techniques without assuming a trusted...

Traditional secure multiparty computation (MPC) protocols presuppose a fixed set of participants throughout the computational process. To address this limitation, Fluid MPC [CRYPTO 2021] presents a dynamic MPC model that allows parties to join or exit during circuit evaluation dynamically. However, existing dynamic MPC protocols can guarantee safety but not liveness within asynchronous networks. This paper introduces ΠAD-MPC, a fully asynchronous dynamic MPC protocol. ΠAD-MPC ensures both...

This paper investigates the open problem of how to construct non-interactive rational proofs. Rational proofs, introduced by Azar and Micali (STOC 2012), are a model of interactive proofs where a computationally powerful server can be rewarded by a weaker client for running an expensive computation $f(x)$. The honest strategy is enforced by design when the server is rational: any adversary claiming a false output $y \neq f(x)$ will lose money on expectation. Rational proof constructions...

In this paper, we introduce a novel approach to Multi-Key Fully Homomorphic Encryption (MK-FHE) that enhances both efficiency and security beyond the capabilities of traditional MK-FHE and MultiParty Computation (MPC) systems. Our method generates a unified key structure, enabling constant ciphertext size and constant execution time for encrypted computations, regardless of the number of participants involved. This approach addresses critical limitations such as ciphertext size expansion,...

Oblivious Pseudorandom Functions (OPRFs) allow a client to evaluate a pseudorandom function (PRF) on her secret input based on a key that is held by a server. In the process, the client only learns the PRF output but not the key, while the server neither learns the input nor the output of the client. The arguably most popular OPRF is due to Naor, Pinkas and Reingold (Eurocrypt 2009). It is based on an Oblivious Exponentiation by the server, with passive security under the Decisional...

We present $\textit{Rhombus}$, a new secure matrix-vector multiplication (MVM) protocol in the semi-honest two-party setting, which is able to be seamlessly integrated into existing privacy-preserving machine learning (PPML) frameworks and serve as the basis of secure computation in linear layers. $\textit{Rhombus}$ adopts RLWE-based homomorphic encryption (HE) with coefficient encoding, which allows messages to be chosen from not only a field $\mathbb{F}_p$ but also a ring...

Vessels can be recognised by their navigation radar due to the characteristics of the emitted radar signal. This is particularly useful if one wants to build situational awareness without revealing one's own presence. Most countries maintain databases of radar fingerprints but will not readily share these due to national security regulations. Sharing of such information will generally require some form of information exchange agreement. However, all parties in a coalition benefit from...

Oblivious Transfer (OT) is one of the fundamental building blocks in cryptography that enables various privacy-preserving applications. Constructing efficient OT schemes has been an active research area. This paper presents three efficient two-round pairing-free k-out-of-N oblivious transfer protocols with standard security. Our constructions follow the minimal communication pattern: the receiver sends k messages to the sender, who responds with n k messages, achieving the lowest data...

We revisit the problem of Authorized Private Set Intersection (APSI), which allows mutually untrusting parties to authorize their items using a trusted third-party judge before privately computing the intersection. We also initiate the study of Partial-APSI, a novel privacy-preserving generalization of APSI in which the client only reveals a subset of their items to a third-party semi-honest judge for authorization. Partial-APSI allows for partial verification of the set, preserving the...

We propose a new cryptographic primitive called ``selective batched identity-based encryption'' (Selective Batched IBE) and its thresholdized version. The new primitive allows encrypting messages with specific identities and batch labels, where the latter can represent, for example, a block number on a blockchain. Given an arbitrary subset of identities for a particular batch, our primitive enables efficient issuance of a single decryption key that can be used to decrypt all ciphertexts...

We show that there exists a unitary quantum oracle relative to which quantum commitments exist but no (efficiently verifiable) one-way state generators exist. Both have been widely considered candidates for replacing one-way functions as the minimal assumption for cryptography—the weakest cryptographic assumption implied by all of computational cryptography. Recent work has shown that commitments can be constructed from one-way state generators, but the other direction has remained open. Our...

Laconic cryptography studies two-message protocols that securely compute on large amounts of data with minimal communication cost. Laconic oblivious transfer (OT) is a central primitive where the receiver's input is a large database $\mathsf{DB}$ and the sender's inputs are two messages $m_0$, $m_1$ along with an index $i$, such that the receiver learns the message determined by the choice bit $\mathsf{DB}_i$. OT becomes even more useful for secure computation when considering its laconic...

As the industry prepares for the transition to post-quantum secure public key cryptographic algorithms, vulnerability analysis of their implementations is gaining importance. A theoretically secure cryptographic algorithm should also be able to withstand the challenges of physical attacks in real-world environments. MAYO is a candidate in the ongoing first round of the NIST post-quantum standardization process for selecting additional digital signature schemes. This paper demonstrates three...

Masking is a sound countermeasure to protect against differential power analysis. Since the work by Balasch et al. in ASIACRYPT 2012, inner product masking has been explored as an alternative to the well known Boolean masking. In CARDIS 2017, Poussier et al. showed that inner product masking achieves higher-order security versus Boolean masking, for the same shared size, in the bit-probing model. Wang et al. in TCHES 2020 verified the inner product masking's security order amplification in...

Quantum computational advantage refers to an existence of computational tasks that are easy for quantum computing but hard for classical one. Unconditionally showing quantum advantage is beyond our current understanding of complexity theory, and therefore some computational assumptions are needed. Which complexity assumption is necessary and sufficient for quantum advantage? In this paper, we show that inefficient-verifier proofs of quantumness (IV-PoQ) exist if and only if...

We present a new and efficient method to obtain circuit privacy for lattice-based linearly homomorphic encryptions (LHE). In particular, our method does not involve noise-flooding with exponetially large errors or iterative bootstrapping. As a direct result, we obtain a semi-honest oblivious linear evaluation (OLE) protocol with the same efficiency, reducing the communication cost of the prior state of the art by 50%. Consequently, the amortized time of our protocol improves the prior work...

Multi-key fully homomorphic encryption (MKFHE), a generalization of fully homomorphic encryption (FHE), enables a computation over encrypted data under multiple keys. The first MKFHE schemes were based on the NTRU primitive, however these early NTRU based FHE schemes were found to be insecure due to the problem of over-stretched parameters. Recently, in the case of standard (non-multi key) FHE a secure version, called FINAL, of NTRU has been found. In this work we extend FINAL to an...

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...

Recent oracle separations [Kretschmer, TQC'21, Kretschmer et. al., STOC'23] have raised the tantalizing possibility of building quantum cryptography from sources of hardness that persist even if the polynomial hierarchy collapses. We realize this possibility by building quantum bit commitments and secure computation from unrelativized, well-studied mathematical problems that are conjectured to be hard for $\mathsf{P}^{\#\mathsf{P}}$ -- such as approximating the permanents of complex Gaussian...

The problem of reliable/secure all-to-all communication over low-degree networks has been essential for communication-local (CL) n-party MPC (i.e., MPC protocols where every party directly communicates only with a few, typically polylogarithmic in n, parties) and more recently for communication over ad hoc networks, which are used in blockchain protocols. However, a limited number of adaptively secure solutions exist, and they all make relatively strong assumptions on the ability of parties...

In the context of secure multiparty computation (MPC) protocols with guaranteed output delivery (GOD) for the honest majority setting, the state-of-the-art in terms of communication is the work of (Goyal et al. CRYPTO'20), which communicates O(n|C|) field elements, where |C| is the size of the circuit being computed and n is the number of parties. Their round complexity, as usual in secret-sharing based MPC, is proportional to O(depth(C)), but only in the optimistic case where there is no...

This paper initiates a concrete-security treatment of two-party secure computation. The first step is to propose, as target, a simple, indistinguishability-based definition that we call InI. This could be considered a poor choice if it were weaker than standard simulation-based definitions, but it is not; we show that for functionalities satisfying a condition called invertibility, that we define and show is met by functionalities of practical interest like PSI and its variants, the two...

We consider the graph-theoretic problem of removing (few) nodes from a directed acyclic graph in order to reduce its depth. While this problem is intractable in the general case, we provide a variety of algorithms in the case where the graph is that of a circuit of fan-in (at most) two, and explore applications of these algorithms to secure multiparty computation with low communication. Over the past few years, a paradigm for low-communication secure multiparty computation has found success...

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...

We witness an increase in applications like cryptocurrency wallets, which involve users issuing signatures using private keys. To protect these keys from loss or compromise, users commonly outsource them to a custodial server. This creates a new point of failure, because compromise of such a server leaks the user’s key, and if user authentication is implemented with a password then this password becomes open to an offline dictionary attack (ODA). A better solution is to secret-share the key...

Modern data analytics requires computing functions on streams of data points from many users that are challenging to calculate, due to both the high scale and nontrivial nature of the computation at hand. The need for data privacy complicates this matter further, as general-purpose privacy-enhancing technologies face limitations in at least scalability or utility. Existing work has attempted to improve this by designing purpose-built protocols for the use case of Private Stream Aggregation;...

A significant body of work in information-theoretic cryptography has been devoted to the fundamental problem of understanding the power of randomness in private computation. This has included both in-depth study of the randomness complexity of specific functions (e.g., Couteau and Ros ́en, ASIACRYPT 2022, gives an upper bound of 6 for n-party $\mathsf{AND}$), and results for broad classes of functions (e.g., Kushilevitz et al. STOC 1996, gives an $O(1)$ upper bound for all functions with...

Private set intersection (PSI) allows two mutually distrusting parties each holding a private set of elements, to learn the intersection of their sets without revealing anything beyond the intersection. Recent work (Badrinarayanan et al., PoPETS'22) initiates the study of updatable PSI (UPSI), which allows the two parties to compute PSI on a regular basis with sets that constantly get updated, where both the computation and communication complexity only grow with the size of the small...

Hardware Trojans (HTs) are a longstanding threat to secure computation. Among different threat models, it is the fabrication-time insertion of additional malicious logic directly into the layout of integrated circuits (ICs) that constitutes the most versatile, yet challenging scenario, for both attackers and defenders. Here, we present a large-scale, first-of-its-kind community effort through red-versus-blue teaming that thoroughly explores this threat. Four independently competing blue...

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...

Garbled circuits are a powerful and important cryptographic primitive, introduced by Yao [FOCS 1986] for secure two-party computation. Beaver, Micali and Rogaway (BMR) [STOCS 1990] extended the garbled circuit technique to construct the first constant-round secure multiparty computation (MPC) protocol. In the BMR protocol, the garbled circuit size grows linearly and the online computation time grows quadratically with the number of parties. Previous solutions to avoid this relied on...

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...

Computationally hard problems based on coding theory, such as the syndrome decoding problem, have been used for constructing secure cryptographic schemes for a long time. Schemes based on these problems are also assumed to be secure against quantum computers. However, these schemes are often considered impractical for real-world deployment due to large key sizes and inefficient computation time. In the recent call for standardization of additional post-quantum digital signatures by the...

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...

In the past decade, tens of homomorphic encryption compilers have been released, and there are good reasons for these compilers to exist. Firstly, homomorphic encryption is a powerful secure computation technique in that it is relatively easy for parties to switch from plaintext computation to secure computations when compared to techniques like secret sharing. However, the technique is mathematically involved and requires expert knowledge to express computations as homomorphic encryption...

Secure join queries over encrypted databases, the most expressive class of SQL queries, have attracted extensive attention recently. The state-of-the-art JXT (Jutla et al. ASIACRYPT 2022) enables join queries on encrypted relational databases without pre-computing all possible joins. However, JXT can merely support join queries over two tables (in encrypted databases) with some high-entropy join attributes. In this paper, we propose an equi-join query protocol over two tables dubbed JXT ,...

Achieving malicious security with high efficiency in dishonest-majority secure multiparty computation is a formidable challenge. The milestone works SPDZ and TinyOT have spawn a large family of protocols in this direction. For boolean circuits, state-of-the-art works (Cascudo et. al, TCC 2020 and Escudero et. al, CRYPTO 2022) have proposed schemes based on reverse multiplication-friendly embedding (RMFE) to reduce the amortized cost. However, these protocols are theoretically described and...

Privacy-Preserving Machine Learning is one of the most relevant use cases for Secure Multiparty Computation (MPC). While private training of large neural networks such as VGG-16 or ResNet-50 on state-of-the-art datasets such as Imagenet is still out of reach, given the performance overhead of MPC, private inference is starting to achieve practical runtimes. However, we show that in contrast to plaintext machine learning, the usage of GPU acceleration for both linear and nonlinear neural...

Self-Sovereign Identity (SSI) empowers individuals and organizations with full control over their data. Decentralized identifiers (DIDs) are at its center, where a DID contains a collection of public keys associated with an entity, and further information to enable entities to engage via secure and private messaging across different platforms. A crucial stepping stone is DIDComm, a cryptographic communication layer that is in production with version 2. Due to its widespread and active...

When integer and rational arithmetics are performed using modular arithmetics over $\mathbb{Z}/q\mathbb{Z}$, overflows naturally occur due to the mismatch between the infinite cardinality of $\mathbb{Z}$ or $\mathbb{Q}$ and the finite cardinality of $\mathbb{Z}/q\mathbb{Z}$. Since $\mathbb{Z}/q\mathbb{Z}$ is also the (sub) message space for many secure computation designs, secure computations of integer and rational arithmetics using these schemes must also consider the overflow...

Secure multiparty computation (MPC) protocols enable $n$ parties, each with private inputs, to compute a given function without leaking information beyond the outputs. One of the main approaches to designing efficient MPC protocols is to use secret sharing. In general, secret sharing based MPC contains three phases: input sharing, circuit evaluation, and output recovery. If the adversary corrupts at most $t$ parties, the protocol typically uses $(t,n)$ threshold secret sharing to share the...

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...

The proliferation of attacks to cloud computing, coupled with the vast amounts of data outsourced to online services, continues to raise major concerns about the privacy for end users. Traditional cryptography can help secure data transmission and storage on cloud servers, but falls short when the already encrypted data needs to be processed by the cloud provider. An emerging solution to this challenge is fully homomorphic encryption (FHE), which enables computations directly on encrypted...

Rollups are special applications on distributed state machines (aka blockchains) for which the underlying state machine only logs, but does not execute transactions. Rollups have become a popular way to scale applications on Ethereum and there is now growing interest in running rollups on Bitcoin. Rollups scale throughput and reduce transaction costs by using auxiliary machines that have higher throughput and lower cost of executing transactions than the underlying blockchain. State updates...

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...

We study the problem of generating public unbiased randomness in a distributed manner within the recent You Only Speak Once (YOSO) framework for stateless multiparty computation, introduced by Gentry et al. in CRYPTO 2021. Such protocols are resilient to adaptive denial-of-service attacks and are, by their stateless nature, especially attractive in permissionless environments. While most works in the YOSO setting focus on independent random corruptions, we consider YOSO protocols with...

Topology-hiding broadcast (THB) enables parties communicating over an incomplete network to broadcast messages while hiding the network topology from within a given class of graphs. Although broadcast is a privacy-free task, it is known that THB for certain graph classes necessitates computational assumptions, even against "honest but curious" adversaries, and even given a single corrupted party. Recent works have tried to understand when THB can be obtained with information-theoretic (IT)...

Verifiable Timed Signatures (VTS) are cryptographic constructs that enable obtaining a signature at a specific time in the future and provide evidence that the signature is legitimate. This framework particularly finds utility in applications such as payment channel networks, multiparty signing operations, or multiparty computation, especially within blockchain architectures. Currently, VTS schemes are based on signature algorithms such as BLS signature, Schnorr signature, and ECDSA. These...

For more than two decades, pairings have been a fundamental tool for designing elegant cryptosystems, varying from digital signature schemes to more complex privacy-preserving constructions. However, the advancement of quantum computing threatens to undermine public-key cryptography. Concretely, it is widely accepted that a future large-scale quantum computer would be capable to break any public-key cryptosystem used today, rendering today's public-key cryptography obsolete and mandating the...

In recent years, formal verification has emerged as a crucial method for assessing security against Side-Channel attacks of masked implementations, owing to its remarkable versatility and high degree of automation. However, formal verification still faces technical bottlenecks in balancing accuracy and efficiency, thereby limiting its scalability. Former tools like maskVerif and CocoAlma are very efficient but they face accuracy issues when verifying schemes that utilize properties of...

A common misconception is that the computational abilities of circuits composed of additions and multiplications are restricted to simple formulas only. Such arithmetic circuits over finite fields are actually capable of computing any function, including equality checks, comparisons, and other highly non-linear operations. While all those functions are computable, the challenge lies in computing them efficiently. We refer to this search problem as arithmetization. Arithmetization is a key...

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,...

Computing the maximum from a list of secret inputs is a widely-used functionality that is employed ei- ther indirectly as a building block in secure computation frameworks, such as ABY (NDSS’15) or directly used in multiple applications that solve optimisation problems, such as secure machine learning or secure aggregation statistics. Incremental distributed point function (I-DPF) is a powerful primitive (IEEE S&P’21) that significantly reduces the client- to-server communication and are...

Akavia, Gentry, Halevi, and Vald introduced the security notion of function-chosen-plaintext-attack (FuncCPA security) for public-key encryption schemes. FuncCPA is defined by adding a functional re-encryption oracle to the IND-CPA game. This notion is crucial for secure computation applications where the server is allowed to delegate a part of the computation to the client. Dodis, Halevi, and Wichs introduced a stronger variant called FuncCPA$^ $. They showed FuncCPA$^ $ implies...

Bottleneck complexity is an efficiency measure of secure multiparty computation (MPC) protocols introduced to achieve load-balancing in large-scale networks, which is defined as the maximum communication complexity required by any one player within the protocol execution. Towards the goal of achieving low bottleneck complexity, prior works proposed MPC protocols for computing symmetric functions in the correlated randomness model, where players are given input-independent correlated...

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...

Clustering is a crucial unsupervised learning method extensively used in the field of data analysis. For analyzing big data, outsourced computation is an effective solution but privacy concerns arise when involving sensitive information. Fully homomorphic encryption (FHE) enables computations on encrypted data, making it ideal for such scenarios. However, existing privacy-preserving clustering based on FHE are often constrained by the high computational overhead incurred from FHE, typically...

Recent advancements in transformers have revolutionized machine learning, forming the core of Large language models (LLMs). However, integrating these systems into everyday applications raises privacy concerns as client queries are exposed to model owners. Secure multiparty computation (MPC) allows parties to evaluate machine learning applications while keeping sensitive user inputs and proprietary models private. Due to inherent MPC costs, recent works introduce model-specific optimizations...

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,...

As large language models (LLMs) continue to gain popularity, concerns about user privacy are amplified, given that the data submitted by users for inference may contain sensitive information. Therefore, running LLMs through secure two-party computation (a.k.a. secure LLM inference) has emerged as a prominent topic. However, many operations in LLMs, such as Softmax and GELU, cannot be computed using conventional gates in secure computation; instead, lookup tables (LUTs) have to be utilized,...

Fully homomorphic encryption (FHE) enables arbitrary computation on encrypted data, but certain applications remain prohibitively expensive in the encrypted domain. As a case in point, comparing two encrypted sets of data is extremely computationally expensive due to the large number of comparison operators required. In this work, we propose a novel methodology for encrypted set similarity inspired by the MinHash algorithm and the CGGI FHE scheme. Doing comparisons in FHE requires...

Homomorphic encryption is a powerful technology that solves key privacy concerns in cloud computing by enabling computation on encrypted data. However, it has not seen widespread adoption due to prohibitively high latencies. In this article, we identify polynomial multiplication as a bottleneck and investigate alternative algorithms to accelerate encrypted computing.

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...

Proving the security of a Multiparty Computation (MPC) protocol is a difficult task. Under the current simulation-based definition of MPC, a security proof consists of a simulator, which is usually specific to the concrete protocol and requires to be manually constructed, together with a theoretical analysis of the output distribution of the simulator and corrupted parties' views in the real world. This presents an obstacle in verifying the security of a given MPC protocol. Moreover, an...

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...

The problem of minimizing the share size of threshold secret-sharing schemes is a basic research question that has been extensively studied. Ideally, one strives for schemes in which the share size equals the secret size. While this is achievable for large secrets (Shamir, CACM '79), no similar solutions are known for the case of binary, single-bit secrets. Current approaches often rely on so-called ramp secret sharing that achieves a constant share size at the expense of a slight gap...

Secure merge refers to the problem of merging two sorted lists. The problem appears in different settings where each list is held by one of two parties, or the lists are themselves shared among two or more parties. The output of a secure merge protocol is secret shared. Each variant of the problem offers many useful applications. The difficulty in designing secure merge protocols vis-a-vis insecure merge protocols (which work in linear time with a single pass over the lists) has to do...

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...

Garbled circuits (GC) are a secure multiparty computation protocol that enables two parties to jointly compute a function using their private data without revealing it to each other. While garbled circuits are proven secure at the protocol level, implementations can still be vulnerable to side-channel attacks. Recently, side-channel analysis of GC implementations has garnered significant interest from researchers. We investigate popular open-source GC frameworks and discover that the AES...

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)...

Privacy is a major concern in large-scale digital applications, such as cloud-computing, machine learning services, and access control. Users want to protect not only their plain data but also their associated attributes (e.g., age, location, etc). Functional encryption (FE) is a cryptographic tool that allows fine-grained access control over encrypted data. However, existing FE fall short as they are either inefficient and far from reality or they leak sensitive user-specific...

Enhancing privacy in federal learning (FL) without considering robustness can create an open door for attacks such as poisoning attacks on the FL process. Thus, addressing both the privacy and security aspects simultaneously becomes vital. Although, there are a few solutions addressing both privacy and security in the literature in recent years, they have some drawbacks such as requiring two non-colluding servers, heavy cryptographic operations, or peer-to-peer communication topology. In...

In this note, we introduce structured-seed local pseudorandom generators, a relaxation of local pseudorandom generators. We provide constructions of this primitive under the sparse-LPN assumption, and explore its implications.

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...

Cryptocurrencies and blockchain technology provide an innovative model for reshaping digital services. Driven by the movement toward Web 3.0, recent systems started to provide distributed services, such as computation outsourcing or file storage, on top of the currency exchange medium. By allowing anyone to join and collect cryptocurrency payments for serving others, these systems create decentralized markets for trading digital resources. Yet, there is still a big gap between the promise of...

Fully homomorphic encryption (FHE) enables secure data processing without compromising data access, but its computational cost and slower execution compared to plaintext operations pose challenges. The growing interest in FHE-based secure computation necessitates the acceleration of homomorphic computations. While existing research primarily targets the reduction of the multiplicative depth (MD) of homomorphic circuits, this paper addresses the trade-off between MD reduction and the increase...

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...

Homomorphic encryption allows for computations on encrypted data without exposing the underlying plaintext, enabling secure and private data processing in various applications such as cloud computing and machine learning. This paper presents a comprehensive mathematical foundation for three prominent homomorphic encryption schemes: Brakerski-Gentry-Vaikuntanathan (BGV), Brakerski-Fan-Vercauteren (BFV), and Cheon-Kim-Kim-Song (CKKS), all based on the Ring Learning with Errors (RLWE) problem....

We study network-agnostic secure multiparty computation with perfect security. Traditionally MPC is studied assuming the underlying network is either synchronous or asynchronous. In a network-agnostic setting, the parties are unaware of whether the underlying network is synchronous or asynchronous. The feasibility of perfectly-secure MPC in synchronous and asynchronous networks has been settled a long ago. The landmark work of [Ben-Or, Goldwasser, and Wigderson, STOC'88] shows that $n...

Given a graph $G(V,E)$, represented as a secret-sharing of an adjacency list, we show how to obliviously convert it into an alternative, MPC-friendly secret-shared representation, so-called $d$-normalized replicated adjacency list (which we abbreviate to $d$-normalized), where the size of our new data-structure is only 4x larger -- compared to the original (secret-shared adjacency list) representation of $G$. Yet, this new data structure enables us to execute oblivious graph algorithms that...

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...