Mixed-Technique, Maliciously-Secure, and Composed Multi-Party Computations.

Arash Afshar, Payman Mohassel, Mike Rosulek

Yulin Wu, Xuan Wang, Willy Susilo +4 more

Minhye Seo

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.

Yan Huang

Fattaneh Bayatbabolghani, Marina Blanton

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.

Nigel Paul Smart