Secure Multi-Party Computation (SMC) allows multiple parties to compute some function of their inputs without disclosing the actual inputs to one another. Secure sum computation is an easily understood example and the component of the various SMC solutions. Secure sum computation allows parties to compute the sum of their individual inputs without disclosing the inputs to one another. In this paper, we propose a modified version of our ck-Secure Sum protocol with more security when a group of the computing parties conspire to know the data of some party.
Secure Multi-Party Computation (SMC) allows multiple parties to compute some function of their inputs without disclosing the actual inputs to one another. Secure sum computation is an easily understood example and the component of the various SMC solutions. Secure sum computation allows parties to compute the sum of their individual inputs without disclosing the inputs to one another. In this paper, we propose a modified version of our ck-Secure Sum protocol with more security when a group of the computing parties conspire to know the data of some party.
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.
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.
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) addresses the problem of jointly computing global functions of private inputs while revealing minimal information about individual data. Two prominent examples of SMC tasks are sealed-bid auction and secure multi-party summation. Existing schemes for quantum auction and quantum summation have largely been developed independently, motivated by distinct applications and employing different computational primitives. In this work, mutual reductions between the two tasks are established for single-item sealed-bid auctions with first-price payment. In one direction, an auction used as a black box computes a secure sum: each party bids a private random key drawn from its input, so the clearing price depends on the inputs only through their sum S. The reduction is perfectly private against colluding semi-honest parties and uses O(S2log(1/δ)) auction rounds, which is optimal, or O(Slog(1/δ)) under coherent access. It can be instantiated with an existing auctioneer-free quantum auction after a one-line change to its disjunction subroutine, which as published leaks occupancy counts. In the other direction, summation with randomized inputs yields threshold disjunctions, and a single search over composite keys locates the highest bid and a winner, revealing nothing else. Because each query consumes the bidders' encodings, a bidder can adapt its effective bid undetected, while a fixed query set restores sealed bidding. Combinatorial auctions and general payment rules lie outside this framework. On a superconducting processor, one instance of each direction is executed, recovering the winner for four-bidder instances with ties and the sum of private inputs from repeated auctions.
In this paper, comparative study of different available Secure Multiparty Computation (SMC) protocols have been addressed. In SMC, a set of parties wishes to jointly compute some function on their inputs. This computation must preserve certain security properties, like privacy and correctness. The general approach for such kind of computation is to make use of trusted third party to do the computation and then announce the result publicly. The major problem with this approach is that it is difficult to find a third party which is trusted by all the parties providing the inputs. This implies that the data of parties must be secured. Security is meant to achieve correctness of the result of computation and keeping the party’s input private even if some of the parties are corrupted.