Secure Multiparty Computation and Differential Privacy
Balamurugan Anandan - Purdue University
Nov 04, 2015Size: 125.4MB
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AbstractSecure multiparty computation (MPC) and differential privacy are two notions of privacy that deal respectively with how and what functions can be privately computed. In this talk, I will first give an overview of MPC and differential privacy. Then, I will show how to build a two party differentially private secure protocol in the presence of semi-honest and malicious adversaries.
Computing a differentially private function using secure function evaluation prevents private information leakage both in the process, and from information present in the function output. However, the very secrecy provided by secure function evaluation poses new challenges if any of the parties are malicious. We then relax the utility requirement of computational differential privacy to reduce computational cost, still giving security with rational adversaries. Finally, we provide a modified two-party computational differential privacy definition and show correctness and security guarantees in the rational setting.
About the SpeakerBalamurugan Anandan is a PhD candidate in Computer Science from Purdue University and works with Prof. Chris Clifton. He received his bachelor's degree in computer science from Kongu Engineering College, India in 2005 and MS in computer science from Purdue University in 2013. His research interests is in the intersection of data mining and privacy, specifically focussing on developing privacy preserving protocols.
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