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Private queries in location based services: anonymizers are not necessary
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International Conference on Management of Data archive
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
Authors
Gabriel Ghinita  National University of Singapore, Singapore, Singapore
Panos Kalnis  National University of Singapore, Singapore, Singapore
Ali Khoshgozaran  University of Southern California, Los Angeles, USA
Cyrus Shahabi  University of Southern California, Los Angeles, USA
Kian-Lee Tan  National University of Singapore, Singapore, Singapore
Sponsors
ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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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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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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CITED BY  14

Collaborative Colleagues:
Gabriel Ghinita: colleagues
Panos Kalnis: colleagues
Ali Khoshgozaran: colleagues
Cyrus Shahabi: colleagues
Kian-Lee Tan: colleagues