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Unformatted text preview: PoolView: Stream Privacy for Grassroots Participatory Sensing Raghu K. Ganti, Nam Pham, Yu-En Tsai, and Tarek F. Abdelzaher Department of Computer Science, University of Illinois, Urbana-Champaign rganti2,nampham2,ytsai20,email@example.com ABSTRACT This paper develops mathematical foundations and architec- tural components for providing privacy guarantees on stream data in grassroots participatory sensing applications, where groups of participants use privately-owned sensors to col- lectively measure aggregate phenomena of mutual interest. Grassroots applications refer to those initiated by members of the community themselves as opposed to by some gov- erning or official entities. The potential lack of a hierar- chical trust structure in such applications makes it harder to enforce privacy. To address this problem, we develop a privacy-preserving architecture, called PoolView , that relies on data perturbation on the client-side to ensure individuals privacy and uses community-wide reconstruction techniques to compute the aggregate information of interest. PoolView allows arbitrary parties to start new services, called pools, to compute new types of aggregate information for their clients. Both the client-side and server-side components of PoolView are implemented and available for download, in- cluding the data perturbation and reconstruction compo- nents. Two simple sensing services are developed for illus- tration; one computes traffic statistics from subscriber GPS data and the other computes weight statistics for a partic- ular diet. Evaluation, using actual data traces collected by the authors, demonstrates the privacy-preserving aggrega- tion functionality in PoolView. Categories and Subject Descriptors G.3 [ Mathematics of Computing ]: Probability and Statis- tics Time series analysis ; K.4.1 [ Computing Milieux ]: Computers and Society Privacy General Terms Algorithms, Design, Experimentation Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SenSys08, November 57, 2008, Raleigh, North Carolina, USA. Copyright 2008 ACM 978-1-59593-990-6/08/11 ...$5.00. Keywords Privacy, Architecture, Data perturbation, Stream privacy, Grassroots participatory sensing 1. INTRODUCTION Much of the past sensor networks research focused on net- working issues; a scope naturally suggested by the name of the discipline. Another very important aspect of distributed sensing, however, is data management . In this paper, we focus on privacy as a category of data management con- cerns in emerging applications. Our work is motivated by the recent surge in distributed collection of data by self- selected participants for the purpose of characterizing aggre-...
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