Privacy-Preserving Algorithms for Distributed Constraint Optimization

Speak:     Dr. Tal Grinshpoun

Time:       14:00-15:00, Dec. 9

Location:  SIST 1A 301

Host:       Prof. Dengji Zhao

Abstract:

Distributed Constraint Optimization (DCOP) is a fundamental Artificial Intelligence model for solving combinatorial optimization problems that are distributed by nature. The main motivation for DCOP research stems from the inherent distributed structure of many real-world problems and the privacy concerns that are associated with this distribution.

Secure multi-party computation (MPC) is a subfield of Cryptography with the goal of creating methods for parties to jointly compute a function over their inputs while keeping those inputs private.

An ongoing line of research of mine aims at solving DCOPs in a privacy-preserving manner, using MPC techniques, and serves as a unique combination between the fields of Artificial Intelligence and Cryptography. In this talk, I will briefly introduce existing (non-private) DCOP solving methods, show some MPC “magic”, and present their successful combination.

This research has been conducted in full collaboration with Prof. Tamir Tassa from the Open University, as well as in partial collaboration with other researchers.

Bio:

Tal Grinshpoun is a Senior Lecturer (Assistant Professor) in the Department of Industrial Engineering and Management, Ariel University, Israel. He earned his B.Sc. in Mathematics and Computer Science, and his M.Sc. and Ph.D. in Computer Science, all from Ben-Gurion University of the Negev, Israel. His main research interests are Artificial Intelligence, Multi-Agent System, Privacy, Operations Research, Scheduling, and Optimization in Transportation..

Sist seminar 18227