A Survey on Location Based Services in Data Mining
Ipsa Das1, Md Imran Alam2, Jayanti Dansana3
1Ipsa Das, M.Tech research scholar, School of Computer Engineering, KIIT University, Bhubaneswar, India.
2Md. Imran Alam, M.Tech research scholar, School of Computer Engineering, KIIT University, Bhubaneswar, India.
3Jayanti Dansana, School of Computer Engineering, KIIT University, Bhubaneswar, India.
Manuscript received on May 04, 2014. | Revised Manuscript received on May 04, 2014. | Manuscript published on May 05, 2014. | PP: 153-158 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2207054214/2014©BEIESP
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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Data privacy has been the primary concern since the distributed database came into the picture. More than two parties have to compile their data for data mining process without revealing to the other parties. Continuous advancement in mobile networks and positioning technologies have created a strong challenge for location-based applications. Challenges resembling location-aware emergency response, location-based advertisement, and location-based entertainment. Privacy protection in pervasive environments has attracted great interests in recent years. Two kinds of privacy issues, location privacy and query privacy, are threatening the security of the users. The novel combined clustering algorithm for protecting location privacy and query privacy, namely ECC, is discussed. ECC applies an iterative K-means clustering method to group the user requests into clusters for providing location safety while utilizing a hierarchical clustering method for preserving the query privacy.
Keywords: Location Based Services (LBSs), K-Anonymity, Location K-Anonymity, Clustering, Clustering Cloak.