scholarly journals Resilient Custody of Crypto-Assets, and Threshold Multisignatures

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1773
Author(s):  
Vincenzo Di Nicola ◽  
Riccardo Longo ◽  
Federico Mazzone ◽  
Gaetano Russo
Keyword(s):  
Ease Of Use ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Key Generation ◽  
Advantages And Disadvantages

Ensuring safe custody of crypto-assets, while at the same time allowing a user to easily access and transfer them, is one of the biggest challenges of this nascent industry. This is even harder considering also the multiple technological implementations behind each crypto-asset. Here we present a survey of the various solutions for this custody problem, comparing advantages and disadvantages. Then we delve deeper into some interesting schemes based on secure multiparty computation, which give a blockchain-agnostic solution that balances security, safety, and transactional ease of use, and in particular, a protocol that enhances practicality by exploiting a party that may stay offline during the key generation.

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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