Privacy preserving distributed optimization using homomorphic encryption

被引：138
作者：
Lu, Yang [1 ]
Zhu, Minghui [1 ]
机构：
[1] Penn State Univ, Sch Elect Engn & Comp Sci, 201 Old Main, University Pk, PA 16802 USA
来源：
AUTOMATICA | 2018年 / 96卷
关键词：
Distributed optimization; Privacy; Homomorphic encryption; FLOW; INFERENCE; SECURE;
D O I：
10.1016/j.automatica.2018.07.005
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码：314 / 325
页数：12
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