Abstract Applying secure multi‐party computation (SMC) to implement secure linear programming is an important problem of SMC. Private feasible solution determination means that one of the participants has a private value and the other participants have some constraints, and they want to cooperatively determine whether the private value satisfies the constraints without disclosing their private information. This problem has important theoretical and practical significance in both scientific research and practice. In this study, the authors propose a protocol based on elliptic curve cryptosystem for this problem and prove that the protocol is secure in the semi‐honest model. Furthermore, threshold decryption elliptic curve cryptosystem is used to privately determine a feasible solution with multiple constraints for two‐party and multi‐party scenarios. The efficiency analysis shows that the protocols developed in this study are simple and efficient. Finally, the authors also show how to use these protocols to solve other SMC problems.
Abstract Applying secure multi‐party computation (SMC) to implement secure linear programming is an important problem of SMC. Private feasible solution determination means that one of the participants has a private value and the other participants have some constraints, and they want to cooperatively determine whether the private value satisfies the constraints without disclosing their private information. This problem has important theoretical and practical significance in both scientific research and practice. In this study, the authors propose a protocol based on elliptic curve cryptosystem for this problem and prove that the protocol is secure in the semi‐honest model. Furthermore, threshold decryption elliptic curve cryptosystem is used to privately determine a feasible solution with multiple constraints for two‐party and multi‐party scenarios. The efficiency analysis shows that the protocols developed in this study are simple and efficient. Finally, the authors also show how to use these protocols to solve other SMC problems.
Secure multi-party computation (SMC) allows multiple parties to jointly and securely compute a function while preserving the privacy of the involved parties. In this regard, homomorphic cryptosystems allow users to perform addition or multiplication operations on encrypted values without having to decrypt the input values. In this paper, we propose both a cryptographic and a non-cryptographic privacy-preserving protocols that allow one party to collaboratively compute a private polynomial function with at least two other parties using semantically secure cryptosystems. In this respect, the function to be evaluated is kept private to the party in question. In addition, we assume that there is no fully trusted party and that all parties are semi-honest. Moreover, we assume that there is no collusion between the different parties. We present the two protocols in question and report on the underlying performance through the presentation of our implementation results. The proposed protocols are relevant in many applications such as evidence sharing in multi-party forensic investigations, situation awareness through the secure sharing in distributed monitoring of plan execution, etc.
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.
Secure multi-party computation (SMC) is a research hotspot in cryptography in recent years, and is also a key technology for information security protection. The interval secure multi-party computation problems have important theoretical significance in cryptography. Previous studies rarely involve secret interval, that is, interval is generated by two-party or multi-party, and any one knows nothing about the interval information. The secret interval problems have important practical significance in real life. This paper mainly studies the privacy-preserving determination of secret interval and threshold. Firstly, for the secret interval is generated by two-party, based on Paillier’s homomorphic encryption, this paper proposes a protocol. Secondly, for the secret interval is generated by multi-party, this paper proposes an optimization protocol that uses the coding principle combined with Lifted ElGamal homomorphic encryption. Thirdly, this paper proves the protocols can resist collusion attacks, and proves they are safe by using the simulation paradigm. Finally, many practical application problems can be solved by using the protocols proposed in this paper.
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.
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 (SMC) is a paradigm that keeps data of individual parties as secret and provides the private data for computation to evaluate some function of their common interest. The outcome of the computation is made available to all the parties. Cloud Computing is a paradigm shift from the distributed computing where an organization uses resources "as service". With the"pay-as-you-go" model, an organization can improve efficiency while minimizing the expenditure and the operation overhead. However, users of the cloud are worrying about the privacy of their data that has been supplied to the cloud provider. The solutions to these problems can be provided by the protocols of the Secured Multi party Cloud Computing (SMCC). In this tutorial, we propose SMC solution techniques that can be embedded while designing architecture of cloud computing especially when multiple cloud users jointly compute some function of their private data inputs. With our experience of developing protocols and devising algorithms for SMC problem we anticipate a crucial role of SMC in cloud computing. In this tutorial, we explore such scenario while discussing various protocols of SMC and their applicability in cloud computing, with the design of SMCC Architecture.