| Private queries in location based services: anonymizers are not necessary |
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International Conference on Management of Data
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
table of contents
Vancouver, Canada
SESSION: Research Session 3: Privacy & Anonymization
table of contents
Pages 121-132
Year of Publication: 2008
ISBN:978-1-60558-102-6
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Authors
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Gabriel Ghinita
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National University of Singapore, Singapore, Singapore
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Panos Kalnis
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National University of Singapore, Singapore, Singapore
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Ali Khoshgozaran
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University of Southern California, Los Angeles, USA
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Cyrus Shahabi
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University of Southern California, Los Angeles, USA
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Kian-Lee Tan
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National University of Singapore, Singapore, Singapore
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Downloads (6 Weeks): 36, Downloads (12 Months): 550, Citation Count: 14
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ABSTRACT
Mobile devices equipped with positioning capabilities (e.g., GPS) can ask location-dependent queries to Location Based Services (LBS). To protect privacy, the user location must not be disclosed. Existing solutions utilize a trusted anonymizer between the users and the LBS. This approach has several drawbacks: (i) All users must trust the third party anonymizer, which is a single point of attack. (ii) A large number of cooperating, trustworthy users is needed. (iii) Privacy is guaranteed only for a single snapshot of user locations; users are not protected against correlation attacks (e.g., history of user movement). We propose a novel framework to support private location-dependent queries, based on the theoretical work on Private Information Retrieval (PIR). Our framework does not require a trusted third party, since privacy is achieved via cryptographic techniques. Compared to existing work, our approach achieves stronger privacy for snapshots of user locations; moreover, it is the first to provide provable privacy guarantees against correlation attacks. We use our framework to implement approximate and exact algorithms for nearest-neighbor search. We optimize query execution by employing data mining techniques, which identify redundant computations. Contrary to common belief, the experimental results suggest that PIR approaches incur reasonable overhead and are applicable in practice.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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G. Aggarwal, N. Mishra, and B. Pinkas. Secure Computation of the k th-Ranked Element. In Proc. of Int. Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT), pages 40--55, 2004.
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2
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Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
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3
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R. Cheng, Y. Zhang, E. Bertino, and S. Prabhakar. Preserving user location privacy in mobile data management infrastructures. In Int. Workshop on Privacy Enhancing Technologies, pages 393--412, 2006.
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4
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5
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C.-Y. Chow and M. F. Mokbel. Enabling Private Continuous Queries for Revealed User Locations. In Proc. of SSTD, pages 258--275, 2007.
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6
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7
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8
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9
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Joan Feigenbaum , Yuval Ishai , Tal Malkin , Kobbi Nissim , Martin Strauss , Rebecca N. Wright, Secure Multiparty Computation of Approximations, Proceedings of the 28th International Colloquium on Automata, Languages and Programming,, p.927-938, July 08-12, 2001
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10
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D. E. Flath. Introduction to Number Theory. John Wiley & Sons, 1988.
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11
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12
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13
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14
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15
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16
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P. Indyk and D. P. Woodruff. Polylogarithmic Private Approximations and Efficient Matching. In Proc. of Theory of Cryptography Conference (TCC), pages 245--264, 2006.
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17
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18
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A. Khoshgozaran and C. Shahabi. Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy. In Proc. of SSTD, pages 239--257, 2007.
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19
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20
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21
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22
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23
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24
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R. Sion and B. Carbunar. On the Computational Practicality of Private Information Retrieval. In Proc. of Network and Distributed System Security Symposium (NDSS), 2007.
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25
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Wai Kit Wong , David Wai-lok Cheung , Ben Kao , Nikos Mamoulis, Secure kNN computation on encrypted databases, Proceedings of the 35th SIGMOD international conference on Management of data, June 29-July 02, 2009, Providence, Rhode Island, USA
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