dc.contributor.authorHo, Shen-Shyang
dc.contributor.authorRuan, Shuhua
dc.date.accessioned2015-01-20T08:17:17Z
dc.date.available2015-01-20T08:17:17Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.identifier.citationHo, S.-S., & Ruan, S. (2013). Preserving privacy for interesting location pattern mining from trajectory data. Transactions on data privacy, 6(1), 87-106.en_US
dc.identifier.issn1888-5063en_US
dc.identifier.urihttp://hdl.handle.net/10220/24703
dc.description.abstractOne main concern for individuals participating in the data collection of personal location history records i.e., trajectories is the disclosure of their location and related information when a user queries for statistical or pattern mining results such as frequent locations derived from these records. In this paper, we investigate how one can achieve the privacy goal that the inclusion of his location history in a statistical database with interesting location mining capability does not substantially increase risk to his privacy. In particular, we propose a e, d-differentially private interesting geographic location pattern mining approach motivated by the sample-aggregate framework. The approach uses spatial decomposition to limit the number of stay points within a localized spatial partition and then followed by density-based clustering. The e, d-differential privacy mechanism is based on translation and scaling insensitive Laplace noise distribution modulated by database instance dependent smoothed local sensitivity. Unlike the database independent e-differential privacy mechanism, the output perturbation from a e, d-differential privacy mechanism depends on a lower local sensitivity resulting in a better query output accuracy and hence, more useful at a higher privacy level, i.e., smaller e. We demonstrate our e, d-differentially private interesting geographic location discovery approach using the region quadtree spatial decomposition followed by the DBSCAN clustering. Experimental results on the real-world GeoLife dataset are used to show the feasibility of the proposed e, d-differentially private interesting location mining approach.en_US
dc.format.extent21 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesTransactions on data privacyen_US
dc.rights© 2013 The Author(s). This paper was published in Transactions on Data Privacy and is made available as an electronic reprint (preprint) with permission of the Author(s). The paper can be found at the following official URL: [http://www.tdp.cat/issues11/abs.a131a13.php].  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Data::Data storage representations
dc.titlePreserving privacy for interesting location pattern mining from trajectory dataen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.versionPublished versionen_US
dc.identifier.urlhttp://www.tdp.cat/issues11/abs.a131a13.php


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