学术报告:Crowd-Empowered Privacy-Preserving Data Aggregation for Mobile Crowdsensing

题目:Crowd-Empowered Privacy-Preserving Data Aggregation for Mobile Crowdsensing

主讲人:Lei Yang(教授、美国内华达州立大学)

日期:2019年1月2日(星期三)

时间:上午10:00am - 11:30am

地点:数据科学与计算机学院 A201

主持:陈旭 教授

摘要:We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for a sensing task. In this framework, the workers are allowed to report privacy-preserving versions of their data to protect their data privacy; and the platform selects workers based on their sensing capabilities, which aims to address the drawbacks of game-theoretic models that cannot ensure the accuracy level of the aggregated result, due to the existence of multiple Nash Equilibria. Observe that in this auction based framework, there exists externalities among workers’ data privacy, because the data privacy of each worker depends on both her injected noise and the total noise in the aggregated result that is intimately related to which workers are selected to fulfill the task. To achieve a desirable accuracy level of the data aggregation in a cost-effective manner, we explicitly characterize the externalities, i.e., the impact of the noise added by each worker on both the data privacy and the accuracy of the aggregated result. Further, we explore the problem structure, characterize the hidden monotonicity property of the problem, and determine the critical bid of workers, which makes it possible to design a truthful, individually rational and computationally efficient incentive mechanism. The proposed incentive mechanism can recruit a set of workers to approximately minimize the cost of purchasing private sensing data from workers subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

个人介绍:Lei Yang is currently an assistant professor in the Department of Computer Science and Engineering at the University of Nevada, Reno. Prior to joining the University, he was an assistant research professor with the School of Electrical Computer and Energy Engineering at Arizona State University. Before that, he was a postdoctoral scholar at Princeton University and Arizona State University. He received the Best Paper Award Runner-up of IEEE INFOCOM 2014.