Privacy preserving distributed optimization using homomorphic encryption

Yang Lu, Minghui Zhu

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)314-325
Number of pages12
JournalAutomatica
Volume96
DOIs
StatePublished - Oct 1 2018

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Cryptography
Computational efficiency

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

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Privacy preserving distributed optimization using homomorphic encryption. / Lu, Yang; Zhu, Minghui.

In: Automatica, Vol. 96, 01.10.2018, p. 314-325.

Research output: Contribution to journalArticle

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