Visible to the public Differentially Private Objective Functions in Distributed Cloud-based OptimizationConflict Detection Enabled

TitleDifferentially Private Objective Functions in Distributed Cloud-based Optimization
Publication TypeConference Paper
Year of Publication2017
AuthorsYu Wang, University of Illinois at Urbana-Champaign, Matthew Hale, University of Illinois at Urbana-Champaign, Magnus Egerstedt, University of Illinois at Urbana-Champaign, Geir Dullerud, University of Illinois at Urbana-Champaign
Conference Name20th World Congress of the International Federations of Automatic Control (IFAC 2017 World Congress)
Date PublishedJuly 2017
Conference LocationToulouse, France
Keywordsscience of security

Abstract--In this work, we study the problem of keeping the objective functions of individual agents "-differentially private in cloud-based distributed optimization, where agents are subject to global constraints and seek to minimize local objective functions. The communication architecture between agents is cloud-based - instead of communicating directly with each other, they oordinate by sharing states through a trusted cloud computer. In this problem, the difficulty is twofold: the objective functions are used repeatedly in every iteration, and the influence of erturbing them extends to other agents and lasts over time. To solve the problem, we analyze the propagation of perturbations on objective functions over time, and derive an upper bound on them. With the upper bound, we design a noise-adding mechanism that randomizes the cloudbased distributed optimization algorithm to keep the individual objective functions "-differentially private. In addition, we study the trade-off between the privacy of objective functions and the performance of the new cloud-based distributed optimization algorithm with noise. We present simulation results to numerically verify the theoretical results presented.

Citation Keynode-39035