Differentially Private Distributed Convex Optimization via Functional Perturbation

We study a class of distributed convex constrained optimization problems where a group of agents aim to minimize the sum of individual objective functions while each desires that any information about its objective function is kept private. We prove the impossibility of achieving differential privac...

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Bibliographic Details

Published in:	IEEE transactions on control of network systems Vol. 5; no. 1; pp. 395 - 408
Main Authors:	Nozari, Erfan, Tallapragada, Pavankumar, Cortes, Jorge
Format:	Journal Article
Language:	English
Published:	IEEE 01.03.2018
Subjects:	Algorithm design and analysis Convex functions Cyber-physical systems differential privacy distributed algorithms/control Heuristic algorithms Hilbert space Linear programming networks of autonomous agents Optimization Privacy
ISSN:	2325-5870, 2372-2533
Online Access:	Get full text
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