Home > Research > Publications & Outputs > Privacy Preserving Distributed Optimization Usi...

Research

Associated organisational unit

Links

Text available via DOI:

Keywords

View graph of relations

Privacy Preserving Distributed Optimization Using Homomorphic Encryption

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Privacy Preserving Distributed Optimization Using Homomorphic Encryption. / Lu, Yang; Zhu, Minghui.
In: Automatica, Vol. 96, 31.10.2018, p. 314-325.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Lu, Y & Zhu, M 2018, 'Privacy Preserving Distributed Optimization Using Homomorphic Encryption', Automatica, vol. 96, pp. 314-325. https://doi.org/10.1016/j.automatica.2018.07.005

APA

Lu, Y., & Zhu, M. (2018). Privacy Preserving Distributed Optimization Using Homomorphic Encryption. Automatica, 96, 314-325. https://doi.org/10.1016/j.automatica.2018.07.005

Vancouver

Lu Y, Zhu M. Privacy Preserving Distributed Optimization Using Homomorphic Encryption. Automatica. 2018 Oct 31;96:314-325. Epub 2018 Jul 20. doi: 10.1016/j.automatica.2018.07.005

Author

Lu, Yang ; Zhu, Minghui. / Privacy Preserving Distributed Optimization Using Homomorphic Encryption. In: Automatica. 2018 ; Vol. 96. pp. 314-325.

Bibtex

@article{f7d6fc93eb5d4bd5b276c01a35d34d1f,
title = "Privacy Preserving Distributed Optimization Using Homomorphic Encryption",
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.",
keywords = "Distributed optimization, Privacy, Homomorphic encryption",
author = "Yang Lu and Minghui Zhu",
year = "2018",
month = oct,
day = "31",
doi = "10.1016/j.automatica.2018.07.005",
language = "English",
volume = "96",
pages = "314--325",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Privacy Preserving Distributed Optimization Using Homomorphic Encryption

AU - Lu, Yang

AU - Zhu, Minghui

PY - 2018/10/31

Y1 - 2018/10/31

N2 - 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.

AB - 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.

KW - Distributed optimization

KW - Privacy

KW - Homomorphic encryption

U2 - 10.1016/j.automatica.2018.07.005

DO - 10.1016/j.automatica.2018.07.005

M3 - Journal article

VL - 96

SP - 314

EP - 325

JO - Automatica

JF - Automatica

SN - 0005-1098

ER -