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Title of Journal: J Supercomput

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Abbravation: The Journal of Supercomputing

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Springer US

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DOI

10.1016/0032-3861(96)00152-8

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1573-0484

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Direct private query in locationbased services wi

Authors: Charles Asanya Ratan Guha
Publish Date: 2014/10/30
Volume: 71, Issue: 2, Pages: 537-573
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Abstract

Private query in locationbased service allows users to request and receive nearest point of interest POI without revealing their location or object received However since the service is customized it requires userspecific information Problems arise when a user due to privacy or security concerns is unwilling to disclose this information Previous solutions to hide them have been found to be deficient and sometimes inefficient In this paper we propose a novel idea that will partition objects into neighborhoods supported by database design that allows a user to retrieve the exact nearest POI without revealing its location or the object retrieved The paper is organized into two parts In the first part we adopted the concept of topological space to generalize object space To help limit information disclosed and minimize transmission cost we create disjointed neighborhoods such that each neighborhood contains no more than one object We organize the database matrix to align with object location in the area For optimization we introduce the concept of kernel in graphical processing unit GPU and we then develop parallel implementation of our algorithm by utilizing the computing power of the streaming multiprocessors of GPU and the parallel computing platform and programming model of Compute Unified Device Architecture CUDA In the second part we study serial implementation of our algorithm with respect to execution time and complexity Our experiment shows a scalable design that is suitable for any population size with minimal impact to user experience We also study GPU–CUDA parallel implementation and compared the performance with CPU serial processing The results show 239times improvement of GPU over CPU To help determine the optimal size for the parameters in our design or similar scalable algorithm we provide analysis and model for predicting GPU execution time based on the size of the chosen parameter

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