Secure Multi-party Computation(SMC) about which people have got some research findings at home and abroad has been a research focus in international cryptographic community recently,and this paper proposes a new protocol for the inverse of n×n matrices by using Oblivious Transfer 1 out of p protocol in the secure two-party computation environment.The protocol can be used to solve the problem securely and more efficiently.Then gives a new protocol for secure two-party linear system of equations based on it.
Secure Multi-party Computation(SMC) about which people have got some research findings at home and abroad has been a research focus in international cryptographic community recently,and this paper proposes a new protocol for the inverse of n×n matrices by using Oblivious Transfer 1 out of p protocol in the secure two-party computation environment.The protocol can be used to solve the problem securely and more efficiently.Then gives a new protocol for secure two-party linear system of equations based on it.
The growth of the Internet has triggered tremendous opportunities for cooperative computation, where people are jointly conducting computation tasks based on the private inputs they each supply. These computations could occur between mutually untrusted parties, or even between competitors. Today, to conduct such computations, one entity must usually know the inputs from all the participants; however if nobody can be trusted enough to know all the inputs, privacy will become a primary concern. This problem is referred to as Secure Multi-party Computation Problem (SMC) in the literature. Research in the SMC area has been focusing on only a limited set of specific SMC problems, while privacy concerned cooperative computations call for SMC studies in a variety of computation domains. About the specific SMC problems, have some result [6,7,8,9,11,12,13], and all the result are concern Two-party computations. Recently, the paper of China has [23~28].
Secure multi-party computation is a focus of international cryptography in recent years. Protocols for Yao's millionaires' problem have become an important building block of many secure multi-party computation protocols. Their efficiency are crucial to the efficiency of many secure multi-party computation protocols. Unfortunately, known protocols for Yao's millionaries' problem have high computational complexity or communication complexity. In this study, based on the 1-out of-m oblivious transfer and set-inclusion problem, we propose a new protocol to solve this problem. This new protocol is very efficient in terms of both computational and communication complexities. Its privacy-preserving property is also proved by simulation patadigm which is generally accepted in the study of secure multi-party computation. We also compare the information leakage of our new protocol and the known protocols.
We propose several practical SMC protocols for privacy-preserving cooperative scientific computations. We consider two important scientific computations which involve linear equations: the linear systems of equations problem and the linear least-square problem. The protocols proposed in this paper achieve acceptable security in the sense of Du-Zhan's paradigm and t-wise collusion-resistance, and their communication complexity is O(tm), where t is a security parameter and m is the total number of participants. The complexity of our protocol is significantly better than the previous result O( m2/μ ) of [4], in which the oblivious transfer protocol is used as an important building block.
As a fundamental part of cryptography, secure multiparty computation (SMC) is a building block of various cryptographic protocols, and it is also a hot topic in the international cryptographic community.In recent years, many SMC problems, such as secret information comparison, secret set problems and secure multiparty computational geometry, have been widely studied.As many practical problems need to be described by rational numbers, it is both theoretically and practically important to study the SMC problems in the rational number field.However, most of the existing researches focus on integers and the studied data are mainly one-dimensional data.There are few researches on secure multiparty computation of multi-dimensional data in the rational number field, but they can’t be generalized.Based on the fractional representation of rational numbers, the new encoding schemes about rational numbers and rational number vectors were proposed, which could encode multi-dimensional data in the rational number field and provided new solutions for other SMC problems in the rational number field.Based on the encoding scheme and one-way hash function, some protocols were designed for equality problems and set problems in the rational number field.These protocols used basic arithmetic operation and hash operation to guarantee efficiency than existing related protocols.And these protocols didn’t limit the range of research data and they were more widely applicable.It proves that these protocols are secure in the semi-honest model using simulation paradigm, and demonstrates the efficiency and the applicability of these protocols by theoretical analysis and experiment.A practical example was also given to illustrate that approaches are more versatile, and they could also be directly used to solve some secure multiparty computational geometry problems in the rational number field.
Secure multi-party computation (SMC) is an emerging topic which has been drawing growing attention during recent decades. There are many examples which show importance of SMC constructions in practice, such as privacy-preserving decision making and machine learning, auctions, private set intersection, and others. In this tutorial, we provide a comprehensive coverage of SMC techniques, starting from precise definitions and fundamental techniques. Consequently, a significant portion of the tutorial focuses on recent advances in general SMC constructions. We cover garbled circuit evaluation (GCE) and linear secret sharing (LSS) which are commonly used for secure two-party and multi-party computation, respectively. The coverage includes both standard adversarial models: semi-honest and malicious. For GCE, we start with the original Yao's garbled circuits construction [30] for semi-honest adversaries and consequently cover its recent optimizations such as the "free XOR,'' the garbled row reduction, the half-gates optimization, and the use of AES NI techniques. We follow with a discussion of techniques for making GCE resilient to malicious behavior, which includes the cut-and-choose approach and additional techniques to deter known attacks in the presence of malicious participants. In addition, we include the-state-of-the-art protocols for oblivious transfer (OT) and OT extension in the presence of semi-honest and malicious users. For LSS, we start from standard solutions for the semi-honest adversarial model including [5, 28] and consequently move to recent efficient constructions for semi-honest and malicious adversarial models. The coverage includes different types of corruption thresholds (with and without honest majority), which imply different guarantees with respect to abort.