Please use this identifier to cite or link to this item: https://doi.org/10.1145/1376616.1376631
Title: Private queries in location based services: Anonymizers are not necessary
Authors: Ghinita, G.
Kalnis, P. 
Khoshgozaran, A.
Shahabi, C.
Tan, K.-L. 
Keywords: Location anonymity
Private information retrieval
Query privacy
Issue Date: 2008
Source: Ghinita, G.,Kalnis, P.,Khoshgozaran, A.,Shahabi, C.,Tan, K.-L. (2008). Private queries in location based services: Anonymizers are not necessary. Proceedings of the ACM SIGMOD International Conference on Management of Data : 121-132. ScholarBank@NUS Repository. https://doi.org/10.1145/1376616.1376631
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. Copyright 2008 ACM.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
URI: http://scholarbank.nus.edu.sg/handle/10635/40422
ISBN: 9781605581026
ISSN: 07308078
DOI: 10.1145/1376616.1376631
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