Multiparty computations in varying contexts

In the specific value computation scenario of blockchain cross-chain data exchange, participants can organize raw data in the form of data pairs, making the computed specific values become key information for cross-chain collaboration. The main challenges faced in this scenario include: the lack of appropriate privacy protection mechanisms leading to easy leakage of sensitive data; the vulnerability of unencrypted or unvalidated data to tampering or malicious attacks; a crisis of trust among users. To address the above problems, this paper introduces secure multi-party computation (MPC) into the cross-chain data exchange process to ensure the fairness of the computation process and protect data privacy. To address the problem of computing the maximum and minimum values of the sums of keyword-corresponding values in cross-chain interactions, it is transformed into the secure computation scheme of computing the maximum and minimum values of the sums of corresponding elements in the intersection of sets (MMSI). A secure computation scheme for maximum and minimum values based on the fully homomorphic NTRU (FH-NTRU) encryption algorithm is proposed. First, a secure computation protocol for MMSI without a universal set under the semi-honest model is proposed. Then, to address potential malicious behaviors in the protocol, a secure computation protocol for MMSI under the malicious model is proposed, using the cut-and-choose method. The protocol under the malicious model is analyzed for correctness and the security of the protocol is proved using the real/ideal model paradigm. Finally, the efficiency analysis and experimental simulations show that the protocol is efficient and reliable, it can resist malicious adversary attacks and ensure the correctness of the computation results while effectively improving the security during cross-chain interactions.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-14629-1.

Secure Multi-Party Computation: Theory, practice and applications

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

Secure multiparty computation (MPC) has recently been increasingly adopted to secure cryptographic keys in enterprises, cloud infrastructure, and cryptocurrency and blockchain-related settings such as wallets and exchanges. Using MPC in blockchains and other distributed systems highlights the need to consider dynamic settings. In such dynamic settings, parties, and potentially even parameters of underlying secret sharing and corruption tolerance thresholds of sub-protocols, may change over the lifetime of the protocol. In particular, stronger threat models – in which mobile adversaries control a changing set of parties (up to t out of n involved parties at any instant), and may eventually corrupt all n parties over the course of a protocol’s execution – are becoming increasingly important for such real world deployments; secure protocols designed for such models are known as Proactive MPC (PMPC).In this work, we construct the first efficient PMPC protocol for dynamic groups (where the set of parties changes over time) secure against a dishonest majority of parties. Our PMPC protocol only requires \(O(n^2)\) (amortized) communication per secret, compared to existing PMPC protocols that require \(O(n^4)\) and only consider static groups with dishonest majorities. At the core of our PMPC protocol is a new efficient technique to perform multiplication of secret shared data (shared using a bivariate scheme) with \(O(n \sqrt{n})\) communication with security against a dishonest majority without requiring pre-computation. We also develop a new efficient bivariate batched proactive secret sharing (PSS) protocol for dishonest majorities, which may be of independent interest. This protocol enables multiple dealers to contribute different secrets that are efficiently shared together in one batch; previous batched PSS schemes required all secrets to come from a single dealer.

Adaptively secure multiparty computation is an essential and fundamental notion in cryptography. In this work we focus on the basic question of constructing a multiparty computation protocol secure against a malicious, adaptive adversary in the stand-alone setting without assuming an honest majority, in the plain model. It has been believed that this question can be resolved by composing known protocols from the literature. We show that in fact, this belief is fundamentally mistaken. In particular, we show:Round inefficiency is unavoidable when using black-box simulation: There does not exist any $$o\frac{n}{\log {n}}$$ round protocol that adaptively securely realizes a natural n-party functionality with a black-box simulator. Note that most previously known protocols in the adaptive security setting relied on black-box simulators.A constant round protocol using non-black-box simulation: We construct a constant round adaptively secure multiparty computation protocol in a setting without honest majority that makes crucial use of non-black box techniques. Taken together, these results give the first resolution to the question of adaptively secure multiparty computation protocols with a malicious dishonest majority in the plain model, open since the first formal treatment of adaptive security for multiparty computation in 1996.

We study a model of fairness in secure computation in which an adversarial party that aborts on receiving output is forced to pay a mutually predefined monetary penalty. We then show how the Bitcoin network can be used to achieve the above notion of fairness in the two-party as well as the multiparty setting (with a dishonest majority). In particular, we propose new ideal functionalities and protocols for fair secure computation and fair lottery in this model.One of our main contributions is the definition of an ideal primitive, which we call \(\mathcal{F}_{\mathrm{CR}}^\star\) (CR stands for “claim-or-refund”), that formalizes and abstracts the exact properties we require from the Bitcoin network to achieve our goals. Naturally, this abstraction allows us to design fair protocols in a hybrid model in which parties have access to the \(\mathcal{F}_{\mathrm{CR}}^\star\) functionality, and is otherwise independent of the Bitcoin ecosystem. We also show an efficient realization of \(\mathcal{F}_{\mathrm{CR}}^\star\) that requires only two Bitcoin transactions to be made on the network.Our constructions also enjoy high efficiency. In a multiparty setting, our protocols only require a constant number of calls to \(\mathcal{F}_{\mathrm{CR}}^\star\) per party on top of a standard multiparty secure computation protocol. Our fair multiparty lottery protocol improves over previous solutions which required a quadratic number of Bitcoin transactions.KeywordsFair exchangeSecure computationBitcoin

We construct a four round secure multip arty computation (MPC) protocol in the plain model that achieves security against any dishonest majority. The security of our protocol relies only on the existence of four round oblivious transfer. This culminates the long line of research on constructing round-efficient MPC from minimal assumptions (at least w.r.t. black-box simulation).

The last ten years have seen a tremendous growth in the interest and practicality of secure multiparty computation (MPC) and its possible applications. Secure MPC is indeed a very hot research topic and recent advances in the field have already been translated into commercial products world-wide. A major pillar in this advance has been in the case of active security with a dishonest majority, mainly due to the SPDZ-line of work protocols. This survey gives an overview of these protocols, with a focus of the original SPDZ paper (Damgård et al. CRYPTO 2012) and its subsequent optimizations.

Trust-aware and incentive-based offloading scheme for secure multi-party computation in Internet of Things

Secure multi-party computation (SMC) is a cryptographic protocol that allows participants to compute the desired output without revealing their inputs. A variety of results related to increasing the efficiency of SMC protocol have been reported, and thus, SMC can be used in various applications. With the SMC protocol in smart grids, it becomes possible to obtain information for load balancing and various statistics, without revealing sensitive user information. To prevent malicious users from tampering with input values, SMC requires cheater detection. Several studies have been conducted on SMC with cheater detection, but none of these has been able to guarantee the fairness of the protocol. In such cases, only a malicious user can obtain a correct output prior to detection. This can be a critical problem if the result of the computation is real-time information of considerable economic value. In this paper, we propose a fair and secure multi-party computation protocol, which detects malicious parties participating in the protocol before computing the final output and prevents them from obtaining it. The security of our protocol is proven in the universal composability framework. Furthermore, we develop an enhanced version of the protocol that is more efficient when computing an average after detecting cheaters. We apply the proposed protocols to a smart grid as an application and analyze their efficiency in terms of computational cost.

Implementing and Analyzing a Recursive Technique for Building Path Oblivious RAM

Secure multi-party computations for privacy-preserving machine learning

In this paper, we propose an improved version of K. Loukopoulos and D.E. Browne’s quantum multiparty computation protocol (Phys. Rev. A 81:062336, 2010). Compared to the previously protocol, the improved protocol can avoid a security flaw and has higher efficiency.

Internet voting is one electronic voting technology that is seen as having the potential to increase voter turnout by making voting more accessible as it allows the voter to vote from anywhere. For Internet voting to become more acceptable to voters, it must satisfy both privacy and verifiability. However, a problem with most current Internet voting systems is they need to trust a third party to store and tally the votes. This need to trust a third party risks the privacy of the votes in the event that the third party becomes malicious. One way to eliminate the need to trust a third party is through secure multiparty computation (SMC). In SMC, a vote is secretly-shared among multiple non-colluding parties. These parties also jointly tally the votes. Since no single party has the copy of all the parts of a vote, it will be impossible for a single party to learn about a vote. With Sharemind SDK, the development of an application system that uses SMC becomes easier. In our work, we discuss how we used Sharemind SDK in the development of an Internet voting system that satisfies both privacy and verifiability.

In a Secure Multiparty Computation (SMC), mutually distrusting parties use cryptographic techniques to cooperatively compute over their private data, in the process each party learns only explicitly revealed outputs. In this paper, we present Wysteria, a high-level programming language for writing SMCs. As with past languages, like Fairplay, Wysteria compiles secure computations to circuits that are executed by an underlying engine. Unlike past work, Wysteria provides support for mixed-mode programs, which combine local, private computations with synchronous SMCs. Wysteria complements a standard feature set with built-in support for secret shares and with wire bundles, a new abstraction that supports generic n-party computations. We have formalized Wysteria, its refinement type system, and its operational semantics. We show that Wysteria programs have an easy-to-understand single-threaded interpretation and prove that this view corresponds to the actual multi-threaded semantics. We also prove type soundness, a property we show has security ramifications, namely that information about one party's data can only be revealed to another via (agreed upon) secure computations. We have implemented Wysteria, and used it to program a variety of interesting SMC protocols from the literature, as well as several new ones. We find that Wysteria's performance is competitive with prior approaches while making programming far easier, and more trustworthy.