Paper Info
Title
Social-Aware Decentralization for Secure and Scalable Multi-party Computations
Abstract
This work studies the problem of MPC decentralization - that is, identifying a set of computing nodes to securely and efficiently execute the multi-party computation protocol (MPC) over a sensitive dataset. To balance between underdecentralization with high risk and over-decentralization with high cost, our unique approach is to add social-awareness, that is, the MPC protocol, running over a social network, is properly decentralized among the computing nodes selected carefully based on their social relationship. The key technical challenge is in estimating the risk of collusion between nodes on whom the computation is run. We propose solutions to estimate the risk of collusion based on (incomplete) social relationship, as well as algorithms for finding the MPC nodes such that the risk of collusion is minimized. We evaluate our methods on several real-world network datasets, and show that they are effective in minimizing the risk levels. This work has potential in enabling efficient privacy-preserving data sharing and computation in emerging big-data federation platforms, in healthcare, financial marketplaces, and other application domains.
Year
DOI
Venue
2017
10.1109/ICDCSW.2017.56
2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW)
Keywords
Field
DocType
Decentralization,multi-party computation,protocol,social network,graph algorithm
Decentralization,Data modeling,Social relationship,Social network,Computer science,Data sharing,Computer network,Collusion,Distributed computing,Scalability,Computation
Conference
ISSN
ISBN
Citations 
1545-0678
978-1-5386-3293-2
0
PageRank 
References 
Authors
0.34
16
2
Name
Order
Citations
PageRank
Yuzhe Tang114721.06
Sucheta Soundarajan212015.00