报告题目：Inferential Privacy with Correlated Sources
报告人：叶方伟，南京航空航天大学计算机科学与技术学院教授
邀请人：唐小虎 教授
报告时间：2025年9月17日下午15:30
报告地点：犀浦校区X9428
摘要：We consider the problem of ON-OFF privacy in which a user is interested in one of n sources available at a server. The user has the choice to turn privacy ON or OFF depending on whether he wants to hide his interest at the time or not. The challenge of allowing the privacy to be toggled between ON and OFF is that the user's online behavior is correlated over time. Therefore, the user cannot simply ignore the privacy requirement when privacy is OFF. We represent the user's correlated requests by an n-state Markov chain. Our goal is to design ON-OFF privacy schemes with optimal download rate that ensure privacy for past and future requests. We devise a polynomial-time algorithm to construct an ON-OFF privacy scheme. Moreover, we present an upper bound on the achievable rate. We show that the proposed scheme is optimal and the upper bound is tight for some special families of Markov chains. We also give an implicit characterization of the optimal achievable rate as a linear programming (LP).
作者简介：叶方伟，国家级青年人才，南京航空航天大学计算机科学与技术学院教授、博导。2013、2018年分别于东南大学、香港中文大学获得学士、博士学位。2018年至2020年任美国罗格斯大学、麻省理工学院博士后研究员。2023年8月加入南京航空航天大学。长期从事信息论、数据隐私与AI安全、低空安全等方向的研究，承担国家重点研发计划项目子课题、江苏省前沿技术研发项目课题、国家自然科学基金青年基金项目，以及美国NSF、NIH、Army Research Lab基金项目等10余项。荣获2023年江苏省科学技术一等奖。目前担任中国电子学会网络空间安全分会、IEEE PES电力系统通信与网络安全技术委员会等专家委员会委员。