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Analysis of Privacy Measures for Multi-Agent and Networked Systems

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Title: Analysis of Privacy Measures for Multi-Agent and Networked Systems
Author(s): Srinivasan, Venkatakumar
Advisor(s): DasGupta, Bhaskar
Contributor(s): Sloan, Robert H; Solworth, Jon A; Venkatakrishnan, V N; Yero, Ismael Gonzalez; DasGupta, Bhaskar
Department / Program: Computer Science
Degree Granting Institution: University of Illinois at Chicago
Degree: PhD, Doctor of Philosophy
Genre: Doctoral
Subject(s): privacy measures, approximate privacy, social network privacy, par, (k,l)-anonymity
Abstract: Privacy Preserving Computation is an important area of research. Quantifying privacy (or loss of) is a crucial part of such research. In this thesis we have provided various techniques for quantifying loss of privacy in networked and multi-agent systems. In the first part of the thesis we investigated approximate privacy model. We identified a protocol that provides constant average privacy approximation ratio for tiling functions. We also provided calculations of average and worst case privacy approxixation ratio of bisection protocols for non-tiling functions. In the second part of the thesis, we formalized problems concerning a privacy measure, (k,l)-anonymity, for quantifying privacy in large networks. We provide non-trivial computational complexity results for effective computation of this measure.
Issue Date: 2017-05-03
Type: Thesis
Date Available in INDIGO: 2017-11-01
Date Deposited: August 201

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