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TAPAS: Trustworthy privacy-aware participatory sensing

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Abstract

With the advent of mobile technology, a new class of applications, called participatory sensing (PS), is emerging, with which the ubiquity of mobile devices is exploited to collect data at scale. However, privacy and trust are the two significant barriers to the success of any PS system. First, the participants may not want to associate themselves with the collected data. Second, the validity of the contributed data is not verified, since the intention of the participants is not always clear. In this paper, we formally define the problem of privacy and trust in PS systems and examine its challenges. We propose a trustworthy privacy-aware framework for PS systems dubbed TAPAS, which enables the participation of the users without compromising their privacy while improving the trustworthiness of the collected data. Our experimental evaluations verify the applicability of our proposed approaches and demonstrate their efficiency.

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Notes

  1. The process of DC-point assignment to a particular participant is equivalent to returning the locations of a set of DC-points to the participant so that the participant can go to those locations and collect the corresponding data (e.g., pictures).

  2. Other distance metrics, such as network distance, can be incorporated as well.

  3. Note that for \(k\) = \(1\), participants also compute their Voronoi cells during their local communication, and send their range queries along with their ASRs to the server. Thus, the assignment is performed once the server simply returns the range query result of every representative participant.

  4. In this paper we have not defined a trust metric to measure the amount of trust we achieve by redundant data collection. Note that defining such a trust metric is non-trivial in a privacy-aware PS and is the focus of our future work.

  5. To the best of our knowledge, PiRi is the only existing approach for a privacy-aware assignment of DC-points to the participants. However, any other privacy-preserving technique for PS systems is also applicable.

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Acknowledgments

This research is supported in part by Award No. 2011-IJ-CX-K054 from National Institute of Justice, Office of Justice Programs, U.S. Department of Justice, as well as by NSF grants CNS-0831505 (CyberTrust) and IIS-1115153, the USC Integrated Media Systems Center (IMSC), and unrestricted cash and equipment gifts from Google, Microsoft and Qualcomm. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the Department of Justice and the National Science Foundation.

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  1. Information Laboratory, Computer Science Department, University of Southern California, Los Angeles, CA, 90089-0781, USA

    Leyla Kazemi & Cyrus Shahabi

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  1. Leyla Kazemi
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  2. Cyrus Shahabi
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Correspondence to Leyla Kazemi.

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Kazemi, L., Shahabi, C. TAPAS: Trustworthy privacy-aware participatory sensing. Knowl Inf Syst 37, 105–128 (2013). https://doi.org/10.1007/s10115-012-0573-y

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