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Private Information Retrieval Techniques for Enabling Location Privacy in Location-Based Services

  • Chapter
Privacy in Location-Based Applications

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5599))

Abstract

The ubiquity of smartphones and other location-aware hand-held devices has resulted in a dramatic increase in popularity of location-based services (LBS) tailored to user locations. The comfort of LBS comes with a privacy cost. Various distressing privacy violations caused by sharing sensitive location information with potentially malicious services have highlighted the importance of location privacy research aiming to protect user privacy while interacting with LBS.

The anonymity and cloaking-based approaches proposed to address this problem cannot provide stringent privacy guarantees without incurring costly computation and communication overhead. Furthermore, they mostly require a trusted intermediate anonymizer to protect a user’s location information during query processing. In this chapter, we review a set of fundamental approaches based on private information retrieval to process range and k-nearest neighbor queries, the elemental queries used in many Location Based Services, with significantly stronger privacy guarantees as opposed to cloaking or anonymity approaches.

This research has been funded in part by NSF grants IIS-0238560 (PECASE), IIS-0534761, IIS-0742811 and CNS-0831505 (CyberTrust), and in part from the METRANS Transportation Center, under grants from USDOT and Caltrans. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Author information

Authors and Affiliations

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

    Ali Khoshgozaran & Cyrus Shahabi

Authors
  1. Ali Khoshgozaran
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  2. Cyrus Shahabi
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Editor information

Editors and Affiliations

  1. Dipartimento di Informatica e Comunicazione (DICO), Universita’ degli Studi di Milano, Via Comelico 39, 20135, Milano, Italy

    Claudio Bettini

  2. George Mason University, 4400 University Drive, 22030-4444, Fairfax, VA, USA

    Sushil Jajodia

  3. Dipartimento di Tecnologie dell’ Informazione, Universita’ degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Pierangela Samarati

  4. Department of Computer Science, University of Vermont, 33 Colchester Avenue, 05405, Burlington, VT, USA

    X. Sean Wang

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Khoshgozaran, A., Shahabi, C. (2009). Private Information Retrieval Techniques for Enabling Location Privacy in Location-Based Services. In: Bettini, C., Jajodia, S., Samarati, P., Wang, X.S. (eds) Privacy in Location-Based Applications. Lecture Notes in Computer Science, vol 5599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03511-1_3

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  • DOI: https://doi.org/10.1007/978-3-642-03511-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03510-4

  • Online ISBN: 978-3-642-03511-1

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