scholarly journals Graph Design for Secure Multiparty Computation over Non-Abelian Groups

2008 ◽  
pp. 37-53 ◽  
Author(s):  
Xiaoming Sun ◽  
Andrew Chi-Chih Yao ◽  
Christophe Tartary
Keyword(s):  
Abelian Groups ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Graph Design

scholarly journals A subexponential construction of graph coloring for multiparty computation

2014 ◽  
Vol 8 (4) ◽  
pp. 363-403
Author(s):  
Hassan Jameel Asghar ◽  
Yvo Desmedt ◽  
Josef Pieprzyk ◽  
Ron Steinfeld
Keyword(s):  
Graph Coloring ◽  
Planar Graphs ◽  
Abelian Groups ◽  
Black Box ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Adversarial Model ◽  
Deterministic Construction

Abstract We show the first deterministic construction of an unconditionally secure multiparty computation (MPC) protocol in the passive adversarial model over black-box non-Abelian groups which is both optimal (secure against an adversary who possesses any $t < \frac{n}{2}$ inputs) and has subexponential complexity of construction based on coloring of planar graphs. More specifically, following the result of Desmedt et al. (2012) that the problem of MPC over non-Abelian groups can be reduced to finding a t-reliable n-coloring of planar graphs, we show the construction of such a graph which allows a path from the input nodes to the output nodes when any t-party subset is in the possession of the adversary. Unlike the deterministic constructions from Desmedt et al. (2012) our construction has subexponential complexity and is optimal at the same time, i.e., it is secure for any $t < \frac{n}{2}$ .

Secure multiparty computation solutions of collection member decision

2013 ◽  
Vol 33 (12) ◽  
pp. 3527-3530
Author(s):  
Yongli DOU ◽  
Haichun WANG ◽  
Jian KANG
Keyword(s):  
Secure Multiparty Computation ◽  
Multiparty Computation

scholarly journals Efficient Secure Multiparty Computation Protocol for Sequencing Problem over Insecure Channel

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Yi Sun ◽  
Qiaoyan Wen ◽  
Yudong Zhang ◽  
Hua Zhang ◽  
Zhengping Jin
Keyword(s):  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Online Auction ◽  
Multiparty Computation ◽  
Sequencing Problem ◽  
The Core ◽  
Secure Protocol ◽  
Core Issue

As a powerful tool in solving privacy preserving cooperative problems, secure multiparty computation is more and more popular in electronic bidding, anonymous voting, and online auction. Privacy preserving sequencing problem which is an essential link is regarded as the core issue in these applications. However, due to the difficulties of solving multiparty privacy preserving sequencing problem, related secure protocol is extremely rare. In order to break this deadlock, this paper first presents an efficient secure multiparty computation protocol for the general privacy-preserving sequencing problem based on symmetric homomorphic encryption. The result is of value not only in theory, but also in practice.

Two-Round Adaptively Secure Multiparty Computation from Standard Assumptions

2018 ◽  
pp. 175-205 ◽  
Author(s):  
Fabrice Benhamouda ◽  
Huijia Lin ◽  
Antigoni Polychroniadou ◽  
Muthuramakrishnan Venkitasubramaniam
Keyword(s):  
Secure Multiparty Computation ◽  
Multiparty Computation

scholarly journals A proposal of privacy preserving reinforcement learning for secure multiparty computation

2017 ◽  
Vol 6 (2) ◽  
pp. 57 ◽  
Author(s):  
Hirofumi Miyajima ◽  
Noritaka Shigei ◽  
Syunki Makino ◽  
Hiromi Miyajima ◽  
Yohtaro Miyanishi ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Distributed Processing ◽  
Data Encryption ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Learning Methods ◽  
Q Learning ◽  
Encryption And Decryption ◽  
Maze Problem ◽  
Learning Data

Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attracts attention, and a lot of studies have been done. Secure multiparty computation (SMC) is one of these methods. Specifically, two learning methods for machine learning (ML) with SMC are known. One is to divide learning data into several subsets and perform learning. The other is to divide each item of learning data and perform learning. So far, most of works for ML with SMC are ones with supervised and unsupervised learning such as BP and K-means methods. It seems that there does not exist any studies for reinforcement learning (RL) with SMC. This paper proposes learning methods with SMC for Q-learning which is one of typical methods for RL. The effectiveness of proposed methods is shown by numerical simulation for the maze problem.

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