Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-01721-6_5
Title: Location diversity: Enhanced privacy protection in location based services
Authors: Xue, M.
Kalnis, P. 
Pung, H.K. 
Issue Date: 2009
Source: Xue, M., Kalnis, P., Pung, H.K. (2009). Location diversity: Enhanced privacy protection in location based services. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5561 LNCS : 70-87. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-01721-6_5
Abstract: Location-based Services are emerging as popular applications in pervasive computing. Spatial k-anonymity is used in Locationbased Services to protect privacy, by hiding the association of a specific query with a specific user. Unfortunately, this approach fails in many practical cases such as: (i) personalized services, where the user identity is required, or (ii) applications involving groups of users (e.g., employees of the same company); in this case, associating a query to any member of the group, violates privacy. In this paper, we introduce the concept of Location Diversity, which solves the above-mentioned problems. Location Diversity improves Spatial k-anonymity by ensuring that each query can be associated with at least ℓ different semantic locations (e.g., school, shop, hospital, etc). We present an attack model that maps each observed query to a linear equation involving semantic locations, and we show that a necessary condition to preserve privacy is the existence of infinite solutions in the resulting system of linear equations. Based on this observation, we develop algorithms that generate groups of semantic locations, which preserve privacy and minimize the expected query processing and communication cost. The experimental evaluation demonstrates that our approach reduces significantly the privacy threats, while incurring minimal overhead. © Springer-Verlag Berlin Heidelberg 2009.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41041
ISBN: 9783642017209
ISSN: 03029743
DOI: 10.1007/978-3-642-01721-6_5
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