Reasonable Estimated K Nearest Neighbor Queries with Locality and Query Privacy

Authors(2) :-Bade Ankamma Rao, Desu Sudhisha

In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the mobile user. In this paper, we study approximate k nearest neighbor (KNN) queries where the mobile user queries the location-based service (LBS) provider about approximate k nearest points of interest (POIs) based on his current location. We propose a basic solution and a generic solution for the mobile user to preserve his location and query privacy in approximate KNN queries. The proposed solutions are mainly built on the Parlier public-key cryptosystem and can provide both location and query privacy. To preserve query privacy, our basic solution allows the mobile user to retrieve one type of POIs, for example, approximate k nearest car parks, without revealing to the LBS provider what type of points is retrieved. Our generic solution can be applied to multiple discrete type attributes of private location-based queries. Compared with existing solutions for KNN queries with location privacy, our solution is more efficient. Experiments have shown that our solution is practical for KNN queries.

Authors and Affiliations

Bade Ankamma Rao
Department of MCA , St. Mary's Group of Institutions, Guntur, Andhra Pradesh, India
Desu Sudhisha
Department of MCA , St. Mary's Group of Institutions, Guntur, Andhra Pradesh, India

Location based query, location and query privacy, private information retrieval, Parlier cryptosystem, RSA.

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 424-433
Manuscript Number : CSEIT1724101
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Bade Ankamma Rao, Desu Sudhisha, "Reasonable Estimated K Nearest Neighbor Queries with Locality and Query Privacy", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.424-433, July-August-2017.
Journal URL : http://ijsrcseit.com/CSEIT1724101

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