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卫健学术沙龙:基于5G+人工智能的心理/精神健康服务与管理体系建设
Gauging spacetime inversions
AI合成化学前沿与战略研讨会暨国家智能化学数据中心启动会
Macro to micro- and nano-scale fluidic engineering for analytical chemistry
报告题目:
Fairness and Partial Fairness in Secure Two-Party Computation
 报告人:
Jonathan Katz
Prof. 
University of Maryland
报告时间:
2010-06-10 14:00
报告地点:
FIT Building 4-603
主办单位:
清华大学理论计算机科学研究中心
  简介:

Abstract

Protocols for secure two-party computation allow users holding inputs x and y, respectively, to compute f(x,y) while preserving privacy, correctness, and more in the presence of malicious behavior. A protocol is *fair* if either both parties learn the output, or else neither of them do.

Cleve (1986) showed that even completely fair computation of boolean XOR is impossible, and since then the question of complete fairness in two-party computation has been considered closed. Cleve's result, however, only says that complete fairness is impossible *in general*. We ask whether there are *any* interesting examples of functions that can be computed with complete fairness, and make the first progress on this question in 20 years. We also consider a (mild) relaxation of fairness, and give a complete characterization of when this notion is achievable. Based on joint work with Carmit Hazay, Dov Gordon, and Yehuda Lindell

Biography

Dr. Jonathan Katz received S.B. in both Chemistry and Mathematics from MIT, M.A. in Chemistry from Columbia University, and M. Phil. in Computer Science from Columbia University. He is an invited participant of DARPA Computer Science Study Group of 2009 and winner of NSF CAREER award, 2005-2010. He is currently an Associate Professor at the Department of Computer Science, University of Maryland, responsible for maintaining a world-class research program in cryptography and information security. He teaches courses in cryptography, theoretical computer science and network security.

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