On Privacy Preserving Data Release of Linear Dynamic Networks

Lu, Yang and Zhu, Minghui (2020) On Privacy Preserving Data Release of Linear Dynamic Networks. Automatica, 115. ISSN 0005-1098

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Abstract

Distributed data sharing in dynamic networks is ubiquitous. It raises the concern that the private information of dynamic networks could be leaked when data receivers are malicious or communication channels are insecure. In this paper, we propose to intentionally perturb the inputs and outputs of a linear dynamic system to protect the privacy of target initial states and inputs from released outputs. We formulate the problem of perturbation design as an optimization problem which minimizes the cost caused by the added perturbations while maintaining system controllability and ensuring the privacy. We analyze the computational complexity of the formulated optimization problem. To minimize the ℓ 0 and ℓ 2 norms of the added perturbations, we derive their convex relaxations which can be efficiently solved. The efficacy of the proposed techniques is verified by a case study on a heating, ventilation, and air conditioning system.

Item Type:
Journal Article
Journal or Publication Title:
Automatica
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
172566
Deposited By:
Deposited On:
20 Jul 2022 13:05
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Jul 2022 13:05