Privacy Preserving Distributed Optimization Using Homomorphic Encryption

Lu, Yang and Zhu, Minghui (2018) Privacy Preserving Distributed Optimization Using Homomorphic Encryption. Automatica, 96. pp. 314-325. ISSN 0005-1098

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Abstract

This paper studies how a system operator and a set of agents securely execute a distributed projected gradient-based algorithm. In particular, each participant holds a set of problem coefficients and/or states whose values are private to the data owner. The concerned problem raises two questions: how to securely compute given functions; and which functions should be computed in the first place. For the first question, by using the techniques of homomorphic encryption, we propose novel algorithms which can achieve secure multiparty computation with perfect correctness. For the second question, we identify a class of functions which can be securely computed. The correctness and computational efficiency of the proposed algorithms are verified by two case studies of power systems, one on a demand response problem and the other on an optimal power flow problem.

Item Type:
Journal Article
Journal or Publication Title:
Automatica
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
172568
Deposited By:
Deposited On:
19 Jul 2022 09:40
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2022 11:36