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Semantic analysis in location privacy preserving
Author(s) -
Xiong Ping,
Zhang Lefeng,
Zhu Tianqing
Publication year - 2015
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3508
Subject(s) - computer science , obfuscation , euclidean distance , location based service , service provider , semantic similarity , metric (unit) , position (finance) , data mining , information retrieval , computer security , service (business) , theoretical computer science , artificial intelligence , computer network , operations management , economy , finance , economics
Summary With the increasing use of location‐based services, location privacy has recently started raising serious concerns. Location perturbation and obfuscation are most widely used for location privacy preserving. To protect a user from being identified, a cloaked spatial region that contains other k − 1 nearest neighbors of the user is submitted to the location‐based service provider, instead of the accurate position. In this paper, we consider the location‐aware applications that services are different among regions. In such scenarios, the semantic distance between users should be considered besides the Euclidean distance for searching the neighbors of a user. We define a novel distance measurement that combines the semantic and the Euclidean distance to address the privacy‐preserving issue in the aforementioned applications. We also present an algorithm k NNH to implement our proposed method. Moreover, we conduct performance study experiments on the proposed algorithm. The experimental results further suggest that the proposed distance metric and the algorithm can successfully retain the utility of the location services while preserving users' privacy. Copyright © 2015 John Wiley & Sons, Ltd.