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International Symposium on Spatial and Temporal Databases

SSTD 2015: Advances in Spatial and Temporal Databases pp 295–312Cite as

Combining Differential Privacy and PIR for Efficient Strong Location Privacy

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9239))

Abstract

Data privacy is a huge concern nowadays. In the context of location based services, a very important issue regards protecting the position of users issuing queries. Strong location privacy renders the user position indistinguishable from any other location. This necessitates that every query, independently of its location, should retrieve the same amount of information, determined by the query with the maximum requirements. Consequently, the processing cost and the response time are prohibitively high for datasets of realistic sizes. In this paper, we propose a novel solution that offers both strong location privacy and efficiency by adjusting the accuracy of the query results. Our framework seamlessly combines the concepts of \(\epsilon \)-differential privacy and private information retrieval (PIR), exploiting query statistics to increase efficiency without sacrificing privacy. We experimentally show that the proposed approach outperforms the current state-of-the-art by orders of magnitude, while introducing only a small bounded error.

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Notes

  1. 1.

    The size of each block depends on the PIR hardware.

  2. 2.

    There is a distinct query plan for every allowed value of k. For ease of presentation, we focus on a single value of k.

  3. 3.

    In this setting, there are several users, each holding a value, and they wish to publish the total sum, so that the value of any user is not revealed, even if an adversary has complete knowledge of all the remaining users.

  4. 4.

    SimpleGeo’s Places, available at http://freegisdata.rtwilson.com/.

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Acknowledgments

This work was supported by GRF grant 618011 from Hong Kong RGC.

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Authors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Eric Fung, Georgios Kellaris & Dimitris Papadias

Authors
  1. Eric Fung
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  2. Georgios Kellaris
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  3. Dimitris Papadias
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Corresponding author

Correspondence to Dimitris Papadias .

Editor information

Editors and Affiliations

  1. Naval Academy Research Institute, Brest naval, France

    Christophe Claramunt

  2. University of Florida, Gainesville, Florida, USA

    Markus Schneider

  3. Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Raymond Chi-Wing Wong

  4. Emory University, Atlanta, USA

    Li Xiong

  5. Gachon University, Gyeonggi-do, Korea (Republic of)

    Woong-Kee Loh

  6. University of Southern California, Los Angeles, USA

    Cyrus Shahabi

  7. Department of Computer Science, Pusan National University, Pusan, Korea (Republic of)

    Ki-Joune Li

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Fung, E., Kellaris, G., Papadias, D. (2015). Combining Differential Privacy and PIR for Efficient Strong Location Privacy. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-22363-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22362-9

  • Online ISBN: 978-3-319-22363-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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