NDLI: Providing K-Anonymity in location based services

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Volume 12, Issue 2, December 2010

Volume 12, Issue 1, June 2010

Providing K-Anonymity in location based services

Mining interesting subgraphs by output space sampling

10th international French speaking conference on knowledge discovery and management (EGC2010)

On a multicriteria clustering approach for attack attribution

Making pattern mining useful

An exploration of climate data using complex networks

Identifying graphs from noisy and incomplete data

A brief survey on sequence classification

Apples-to-apples in cross-validation studies

Survey on social tagging techniques

Providing K-Anonymity in location based services

Content Provider	ACM Digital Library
Author	Kalnis, Panos Gkoulalas-Divanis, Aris Verykios, Vassilios S.
Abstract	The offering of anonymity in relational databases has attracted a great deal of attention in the database community during the last decade [4]. Among the different solution approaches that have been proposed to tackle this problem, K-anonymity has received increased attention and has been extensively studied in various forms. New forms of data that come into existence, like location data capturing user movement, pave the way for the offering of cutting edge services such as the prevailing Location Based Services (LBSs). Given that these services assume an in-depth knowledge of the mobile users' whereabouts it is certain that the assumed knowledge may breach the privacy of the users. Thus, concrete approaches are necessary to preserve the anonymity of the mobile users when requesting LBSs. In this work, we survey recent advancements for the offering of K-anonymity in LBSs. Most of the approaches that have been proposed heavily depend on a trusted server component -- that acts as an intermediate between the end user and the service provider - to preserve the anonymity of the former entity. Existing approaches are partitioned in three categories: (a) historical K-anonymity, (b) location K-anonymity, and (c) trajectory K-anonymity. In each of these categories we present some of the most prevalentmethodologies that have been proposed and highlight their operation.
Starting Page	3
Ending Page	10
Page Count	8
File Format	PDF
ISSN	19310145 19310153
DOI	10.1145/1882471.1882473
Journal	ACM SIGKDD Explorations Newsletter (SKDD)
Volume Number	12
Issue Number	1
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2000-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article

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