Fog-enabled secure multiparty computation based aggregation scheme in smart grid

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.

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Two-Round Adaptively Secure Multiparty Computation from Standard Assumptions

Theory of Cryptography - Lecture Notes in Computer Science ◽

10.1007/978-3-030-03807-6_7 ◽

2018 ◽

pp. 175-205 ◽

Cited By ~ 5

Author(s):

Fabrice Benhamouda ◽

Huijia Lin ◽

Antigoni Polychroniadou ◽

Muthuramakrishnan Venkitasubramaniam

Keyword(s):

Secure Multiparty Computation ◽

Multiparty Computation

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Theoretical Foundations for Mobile Target Defense: Proactive Secret Sharing and Secure Multiparty Computation

Lecture Notes in Computer Science - From Database to Cyber Security ◽

10.1007/978-3-030-04834-1_23 ◽

2018 ◽

pp. 470-486

Author(s):

Karim Eldefrawy ◽

Rafail Ostrovsky ◽

Moti Yung

Keyword(s):

Secret Sharing ◽

Secure Multiparty Computation ◽

Multiparty Computation ◽

Proactive Secret Sharing ◽

Mobile Target ◽

Theoretical Foundations

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A proposal of privacy preserving reinforcement learning for secure multiparty computation

Artificial Intelligence Research ◽

10.5430/air.v6n2p57 ◽

2017 ◽

Vol 6 (2) ◽

pp. 57 ◽

Cited By ~ 2

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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