Secure aggregation is the distributed task of securely computing a sum of values (or a vector of values) held by a set of parties, revealing only the output (i.e., the sum) in the computation. Existing protocols, such as Prio (NDSI’17), Prio+ (SCN’22), Elsa (S&P’23), and Whisper (S&P’24), support secure aggregation with input validation to ensure inputs belong to a specified domain. However, when malicious servers are present, these protocols primarily guarantee privacy but not input...

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...

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...

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...

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...

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...

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...

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...

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...

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. This work focuses on improving the multiple-aggregator model. Existing multiple-aggregator protocols such as Prio (NSDI 2017), Prio+ (SCN 2022), Elsa (S&P 2023) either offer robustness only in the...

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,...

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...

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...

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,...

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...

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...

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...

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...

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...

Secure equality testing and comparison are two important primitives that have been widely used in many secure computation scenarios, such as privacy-preserving machine learning, private set intersection, secure data mining, etc. In this work, we propose new constant-round two-party computation (2PC) protocols for secure equality testing and secure comparison. Our protocols are designed in the online/offline paradigm. Theoretically, for 32-bit integers, the online communication for our...

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...

Multiple works have designed or used maliciously secure honest majority MPC protocols over $\mathbb{Z}_{2^k}$ using replicated secret sharing (e.g. Koti et al. USENIX'21). A recent trend in the design of such MPC protocols is to first execute a semi-honest protocol, and then use a check that verifies the correctness of the computation requiring only sublinear amount of communication in terms of the circuit size. The so-called Galois ring extensions are needed in order to execute such checks...

This paper introduces a novel protocol for privacy-preserving biometric identification, named Monchi, that combines the use of homomorphic encryption for the computation of the identification score with function secret sharing to obliviously compare this score with a given threshold and finally output the binary result. Given the cost of homomorphic encryption, BFV in this solution, we study and evaluate the integration of two packing solutions that enable the regrouping of multiple...

Secure two-party computation allows two mutually distrusting parties to compute a joint function over their inputs, guaranteeing properties such as input privacy or correctness. For many tasks, such as joint computation of statistics, it is important that when one party receives the result of the computation, the other party also receives the result. Unfortunately, this property, which is called fairness, is unattainable in the two-party setting for arbitrary functions. So weaker...

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...

Running machine learning algorithms on encrypted data is a way forward to marry functionality needs common in industry with the important concerns for privacy when working with potentially sensitive data. While there is already a growing field on this topic and a variety of protocols, mostly employing fully homomorphic encryption or performing secure multiparty computation (MPC), we are the first to propose a protocol that makes use of a specialized encryption scheme that allows to do secure...

At S$\&$P 2023, a family of secure three-party computing protocols called Bicoptor was proposed by Zhou et al., which is used to compute non-linear functions in privacy preserving machine learning. In these protocols, two parties $P_0, P_1$ respectively hold the corresponding shares of the secret, while a third party $P_2$ acts as an assistant. The authors claimed that neither party in the Bicoptor can independently compromise the confidentiality of the input, intermediate, or output. In...

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...

Policy-compliant signatures (PCS) are a recently introduced primitive by Badertscher et al. [TCC 2021] in which a central authority distributes secret and public keys associated with sets of attributes (e.g., nationality, affiliation with a specific department, or age) to its users. The authority also enforces a policy determining which senders can sign messages for which receivers based on a joint check of their attributes. For example, senders and receivers must have the same nationality,...

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...

Secure two-party computation (2PC) in the RAM model has attracted huge attention in recent years. Most existing results only support semi-honest security, with the exception of Keller and Yanai (Eurocrypt 2018) with very high cost. In this paper, we propose an efficient RAM-based 2PC protocol with active security and one-bit leakage. 1) We propose an actively secure protocol for distributed point function (DPF), with one-bit leakage, that is essentially as efficient as the...

In this work, we present novel protocols over rings for semi-honest secure three-party computation (3PC) and malicious four-party computation (4PC) with one corruption. While most existing works focus on improving total communication complexity, challenges such as network heterogeneity and computational complexity, which impact MPC performance in practice, remain underexplored. Our protocols address these issues by tolerating multiple arbitrarily weak network links between parties...

A recent work by Ball, Li, Lin, and Liu [Eurocrypt'23] presented a new instantiation of the arithmetic garbling paradigm introduced by Applebaum, Ishai, and Kushilevitz [FOCS'11]. In particular, Ball et al.'s garbling scheme is the first constant-rate garbled circuit over large enough bounded integer computations, inferring the first constant-round constant-rate secure two-party computation (2PC) over bounded integer computations in the presence of semi-honest adversaries. The main source...

Motivated by the need for a massively decentralized network concurrently servicing many clients, we present novel low-overhead UC-secure, publicly verifiable, threshold ECDSA protocols with identifiable abort. For the first time, we show how to reduce the message complexity from O(n^2) to O(n) and the computational complexity from O(n) to practically O(1) (per party, where n is the number of parties). We require only a broadcast channel for communication. Therefore, we natively support...

We provide a novel view of public-key cryptography by showing full equivalences of certain primitives to "hard" monoid actions. More precisely, we show that key exchange and two-party computation are exactly equivalent to monoid actions with certain structural and hardness properties. To the best of our knowledge, this is the first "natural" characterization of the mathematical structure inherent to any key exchange or two-party computation protocol, and the first explicit proof of the...

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,...

Pseudorandom Correlation Functions (PCFs) allow two parties, given correlated evaluation keys, to locally generate arbitrarily many pseudorandom correlated strings, e.g. Oblivious Transfer (OT) correlations, which can then be used by the two parties to jointly run secure computation protocols. In this work, we provide a novel and simple approach for constructing PCFs for OT correlation, by relying on constrained pseudorandom functions for a class of constraints containing a weak...

Laconic cryptography enables secure two-party computation (2PC) on unbalanced inputs with asymptotically-optimal communication in just two rounds of communication. In particular, the receiver (who sends the first-round message) holds a long input and the sender (who sends the second-round message) holds a short input, and the size of their communication to securely compute a function on their joint inputs only grows with the size of the sender's input and is independent of the receiver's...

We propose a solution for user privacy-oriented privacy-preserving data aggregation with multiple data customers. Most existing state-of-the-art approaches present too much importance on performance efficiency and seem to ignore privacy properties except for input privacy. Most solutions for data aggregation do not generally discuss the users’ birthright, namely their privacy for their own data control and anonymity when they search for something on the browser or volunteer to participate in...

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...

Garbling schemes allow to garble a circuit $C$ and an input $x$ such that $C(x)$ can be computed while hiding both $C$ and $x$. In the context of adaptive security, an adversary specifies the input to the circuit after seeing the garbled circuit, so that one can pre-process the garbling of $C$ and later only garble the input $x$ in the online phase. Since the online phase may be time-critical, it is an interesting question how much information needs to be transmitted in this phase and...

A Distributed Oblivious RAM is a multi-party protocol that securely implements a RAM functionality on secret-shared inputs and outputs. This paper presents two DORAMs in the semi-honest honest-majority 3-party setting which are information-theoretically secure and whose communication costs are asymptotic improvements over previous work. Let $n$ be the number of memory locations and let $d$ be the bit-length of each location. The first, MetaDORAM1, is \emph{statistically} secure, with...

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...

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...

Private Set Intersection Cardinality(PSI-CA) is a type of secure two-party computation. It enables two parties, each holding a private set, to jointly compute the cardinality of their intersection without revealing any other private information about their respective sets. In this paper, we manage to break two PSI-CA protocols by recovering the specific intersection items in polynomial time. Among them, the PSI-CA protocol proposed by De Cristofaro et al. in 2012 is the most popular...

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...

Abstract—Large transformer-based models have realized state- of-the-art performance on lots of real-world tasks such as natural language processing and computer vision. However, with the increasing sensitivity of the data and tasks they handle, privacy has become a major concern during model deployment. In this work, we focus on private inference in two-party settings, where one party holds private inputs and the other holds the model. We introduce BumbleBee, a fast and...

Private Set Intersection (PSI) is a well-studied secure two-party computation problem in which a client and a server want to compute the intersection of their input sets without revealing additional information to the other party. With this work, we present nested Cuckoo hashing, a novel hashing approach that can be combined with additively homomorphic encryption (AHE) to construct an efficient PSI protocol for unbalanced input sets. We formally prove the security of our protocol against...

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...

In the setting of solitary output computations, only a single designated party learns the output of some function applied to the private inputs of all participating parties with the guarantee that nothing beyond the output is revealed. The setting of solitary output functionalities is a special case of secure multiparty computation, which allows a set of mutually distrusting parties to compute some function of their private inputs. The computation should guarantee some security properties,...

In secure multiparty computation (MPC), the goal is to allow a set of mutually distrustful parties to compute some function of their private inputs in a way that preserves security properties, even in the face of adversarial behavior by some of the parties. However, classical security definitions do not pose any privacy restrictions on the view of honest parties. Thus, if an attacker adversarially leaks private information to honest parties, it does not count as a violation of privacy. This...

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...

In this work we study the problem of minimizing the round complexity for securely evaluating multiparty functionalities while making black-box use of polynomial time assumptions. In Eurocrypt 2016, Garg et al. showed that, assuming all parties have access to a broadcast channel, then at least four rounds of communication are required to securely realize non-trivial functionalities in the plain model. A sequence of works follow-up the result of Garg et al. matching this lower bound under a...

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...

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...

Protocols with \emph{publicly verifiable covert (PVC) security} offer high efficiency and an appealing feature: a covert party may deviate from the protocol, but with a probability (\eg $90\%$, referred to as the \emph{deterrence factor}), the honest party can identify this deviation and expose it using a publicly verifiable certificate. These protocols are particularly suitable for practical applications involving reputation-conscious parties. However, in the cases where misbehavior goes...

Web users can gather data from secure endpoints and demonstrate the provenance of sensitive data to any third party by using privacy-preserving TLS oracles. In practice, privacy-preserving TLS oracles are practical in verifying private data up to 1 kB in size selectively, which limits their applicability to larger sensitive data sets. In this work, we introduce a new oracle protocol for TLS, which reaches new scales in selectively verifying the provenance of confidential web data. The...

Reversed multiplication friendly embedding (RMFE) amortization has been playing an active role in the state-of-the-art constructions of MPC protocols over rings (in particular, the ring $\mathbb{Z}_{2^k}$). As far as we know, this powerful technique has NOT been able to find applications in the crown jewel of two-party computation, the non-interactive secure computation (NISC), where the requirement of the protocol being non-interactive constitutes a formidable technical bottle-neck. We...

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...

The edit distance is a metric widely used in genomics to measure the similarity of two DNA chains. Motivated by privacy concerns, we propose a 2PC protocol to compute the edit distance while preserving the privacy of the inputs. Since the edit distance algorithm can be expressed as a mixed-circuit computation, our approach uses protocols based on secret-sharing schemes like Tinier and SPD$\mathbb{Z}_{2^k}$; and also daBits to perform domain conversion and edaBits to perform arithmetic...

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...

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...

As a fundamental operation in fixed-point arithmetic, truncation can bring the product of two fixed-point integers back to the fixed-point representation. In large-scale applications like privacy-preserving machine learning, it is essential to have faithful truncation that accurately eliminates both big and small errors. In this work, we improve and extend the results of the oblivious transfer based faithful truncation protocols initialized by Cryptflow2 (Rathee et al., CCS 2020)....

The number-theoretic literature has long studied the question of distributions of sequences of quadratic residue symbols modulo a prime number. In this paper, we present an efficient algorithm for generating primes containing chosen sequences of quadratic residue symbols and use it as the basis of a method extending the functionality of additively homomorphic cryptosystems. We present an algorithm for encoding a chosen Boolean function into the public key and an efficient two-party...

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...

Private computation on (labeled) set intersection (PCSI/PCLSI) is a secure computation protocol that allows two parties to compute fine-grained functions on set intersection, including cardinality, cardinality-sum, secret shared intersection, and arbitrary functions. Recently, some computationally efficient PCSI protocols have emerged, but a limitation of these protocols is the communication complexity, which scales (super)-linear with the size of the large set. This is of particular concern...

Consider the standard setting of two-party computation where the sender has a secret function $f$ and the receiver has a secret input $x$ and the output $f(x)$ is delivered to the receiver at the end of the protocol. Let us consider the unidirectional message model where only one party speaks in each round. In this setting, Katz and Ostrovsky (Crypto 2004) showed that at least four rounds of interaction between the parties are needed in the plain model (i.e., no trusted setup) if the...

Transport Layer Security (TLS) establishes an authenticated and confidential channel to deliver data for almost all Internet applications. A recent work (Zhang et al., CCS'20) proposed a protocol to prove the TLS payload to a third party, without any modification of TLS servers, while ensuring the privacy and originality of the data in the presence of malicious adversaries. However, it required maliciously secure Two-Party Computation (2PC) for generic circuits, leading to significant...

We study secure multi-party computation (MPC) protocols for branching circuits that contain multiple sub-circuits (i.e., branches) and the output of the circuit is that of a single active branch. Crucially, the identity of the active branch must remain hidden from the protocol participants. While such circuits can be securely computed by evaluating each branch and then multiplexing the output, such an approach incurs a communication cost linear in the size of the entire circuit. To...

A zero-knowledge proof is a cryptographic protocol where a prover can convince a verifier that a statement is true, without revealing any further information except for the truth of the statement. More precisely, if $x$ is a statement from an NP language verified by an efficient machine $M$, then a zero-knowledge proof aims to prove to the verifier that there exists a witness $w$ such that $M(x,w)=1$, without revealing any further information about $w$. The proof is a proof of knowledge,...

Topology-Hiding Computation (THC) enables parties to securely compute a function on an incomplete network without revealing the network topology. It is known that secure computation on a complete network can be based on oblivious transfer (OT), even if a majority of the participating parties are corrupt. In contrast, THC in the dishonest majority setting is only known from assumptions that imply (additively) homomorphic encryption, such as Quadratic Residuosity, Decisional Diffie-Hellman,...

Secure computation often benefits from the use of correlated randomness to achieve fast, non-cryptographic online protocols. A recent paradigm put forth by Boyle $\textit{et al.}$ (CCS 2018, Crypto 2019) showed how pseudorandom correlation generators (PCG) can be used to generate large amounts of useful forms of correlated (pseudo)randomness, using minimal interactions followed solely by local computations, yielding silent secure two-party computation protocols (protocols where the...

The security and usability of cryptocurrencies and other blockchain-based applications depend on the secure management of cryptographic keys. However, current approaches for managing these keys often rely on third parties, trusted to be available at a minimum, and even serve as custodians in some solutions, creating single points of failure and limiting the ability of users to fully control their own assets. In this work, we introduce the concept of unstoppable wallets, which are...

We initiate the study of the network agnostic MPC protocols with statistical security. Network agnostic protocols give the best possible security guarantees irrespective of the underlying network type. We consider the general-adversary model, where the adversary is characterized by an adversary structure which enumerates all possible candidate subsets of corrupt parties. The $\mathcal{Q}^{(k)}$ condition enforces that the union of no $k$ subsets from the adversary structure covers the party...

The computational overhead of a cryptographic task is the asymptotic ratio between the computational cost of securely realizing the task and that of realizing the task with no security at all. Ishai, Kushilevitz, Ostrovsky, and Sahai (STOC 2008) showed that secure two-party computation of Boolean circuits can be realized with constant computational overhead, independent of the desired level of security, assuming the existence of an oblivious transfer (OT) protocol and a local...

We propose a novel protocol, Falkor, for secure aggregation for Federated Learning in the multi-server scenario based on masking of local models via a stream cipher based on AES in counter mode and accelerated by GPUs running on the aggregating servers. The protocol is resilient to client dropout and has reduced clients/servers communication cost by a factor equal to the number of aggregating servers (compared to the naïve baseline method). It scales simultaneously in the two major...

A Distributed Key Generation (DKG) protocol is an essential component of threshold cryptography. DKGs enable a group of parties to generate a secret and public key pair in a distributed manner so that the secret key is protected from being exposed, even if a certain number of parties are compromised. Robustness further guarantees that the construction of the key pair is always successful, even if malicious parties try to sabotage the computation. In this paper, we construct two efficient...

The applications of Hierarchical Deterministic Wallet are rapidly growing in various areas such as cryptocurrency exchanges and hardware wallets. Improving privacy and security is more important than ever. In this study, we proposed a protocol that fully support a two-party computation of BIP32. Our protocol, similar to the distributed key generation, can generate each party’s secret share, the common chain-code, and the public key without revealing a seed and any descendant private keys. We...

The bottleneck-complexity (BC) of secure multiparty computation (MPC) protocols is a measure of the maximum number of bits which are sent and received by any party in a protocol. As the name suggests, the goal of studying BC-efficient protocols is to increase overall efficiency by making sure that the workload in the protocol is somehow "amortized'' by the protocol participants. Orlandi et al. (PKC 2022) initiated the study of BC-efficient protocols from simple assumptions in the...

This paper presents Griffin, an extension of the mixed-scheme single-key homomorphic encryption framework Pegasus (Lu et al., IEEE S&P’21) to a Multi-Key Homomorphic Encryption (MKHE) scheme with applications to secure computation. MKHE is a generalized notion of Homomorphic Encryption (HE) that allows for operations on ciphertexts encrypted under different keys. However, an efficient approach to evaluate both polynomial and non-polynomial functions on encrypted data in MKHE has not yet been...

Secure aggregation is commonly used in federated learning (FL) to alleviate privacy concerns related to the central aggregator seeing all parameter updates in the clear. Unfortunately, most existing secure aggregation schemes ignore two critical orthogonal research directions that aim to (i) significantly reduce client-server communication and (ii) mitigate the impact of malicious clients. However, both of these additional properties are essential to facilitate cross-device FL with thousands...

Motivated by the privacy requirements in network intrusion detection and DNS policy checking, we have developed a suite of protocols and algorithms for regular expression matching with enhanced privacy: - A new regular expression matching algorithm that is oblivious to the input strings, of which the complexity is only $O(mn)$ where $m$ and $n$ are the length of strings and the regular expression respectively. It is achieved by exploiting the structure of the Thompson nondeterministic...