题目：Information Design under Privacy Constraints

时间：2024年10月29日14:00-15:30

地点：教二2114

汇报人：郑舒冉（清华大学交叉信息研究院助理教授）

主持人：邝仲弘（中国人民大学经济学院副教授）

摘要：The talk will be focusing on information design under privacy constraints. Information disclosure can compromise privacy when revealed information is correlated with private information. In this first part of the talk, we consider the notion of inferential privacy, which measures privacy leakage by bounding the inferential power a Bayesian adversary can gain by observing a released signal. Our goal is to devise an inferentially-private private information structure that maximizes the informativeness of the released signal, following the Blackwell ordering principle, while adhering to inferential privacy constraints. In this second part of the talk, we study Bayesian persuasion under differential privacy constraints, where the sender must design an optimal signaling scheme for persuasion while guaranteeing the privacy of each agent's private information in the database. To understand how privacy constraints affect information disclosure, we explore two perspectives within Bayesian persuasion: one views the mechanism as releasing a posterior about the private data, while the other views it as sending an action recommendation.

个人简介：郑舒冉是清华大学交叉信息研究院助理教授。她在2022年于哈佛大学取得计算机科学博士学位，后在2023年于卡内基梅隆大学担任博士后研究员。在2022年秋，郑舒冉于谷歌纽约研究院担任博士生研究员。她的主要研究方向为计算经济学，研究目标是利用经济学工具理解数据和信息在计算机科学问题中的价值。