SPATIAL LOCATION OF ASSURANCE WITH WITNESS OF MUTUAL PROOFS PROVIDING PRIVACY FOR MOBILE USERS
Journal: International Journal of Advanced Research (Vol.7, No. 2)Publication Date: 2019-02-10
Authors : Gadipalli Bhavani; Vishwesh Nagamalla.;
Page : 1050-1057
Keywords : Location-based services (LBS) outsourced encrypted data privacy-enhancing technology spatial range query.;
Abstract
In our safe tourist application or travelling we aims to outsource the lbs data from the lbs provider to the cloud and from the cloud to the lbs provider which protects the privacy related issues of the lbs data. Initially lbs user query for a place to the lbs provider, lbs provider in turn upload the details to the cloud but in the form of encrypted text to prevent the cloud from stealing the data. Lbs users in turn decrypt the details by the personal password send by the lbs provider to the lbs user. When the query of the lbs user matches the details in the cloud the lbs user will retrieve the details and make use of it. In this application it is shown with the demo of a tourist requesting for tourist places tourist is the lbs user and admin is the lbs provider .With the pervasiveness of smart phones, location based services (LBS) have received considerable attention and become more popular and vital recently. However, the use of LBS also poses a potential threat to user?s location privacy. In this paper, aiming at spatial range query, popular LBS providing information About POIs (Points of Interest), we present an efficient and privacy-preserving location based query solution, called SPATIAL RANGE. To reduce query latency, we further design a privacy-preserving tree index structure in SPATIAL RANGE. Detailed security analysis confirms the security properties of SPATIAL RANGE. In addition, extensive experiments are conducted, and the results demonstrate that spatial range is very efficient in privacy preserving spatial range query over outsourced encrypted data. In particular, for a mobile LBS user using an Android phone, around 0.9 second is needed to generate a query; and it also only requires a commodity workstation, which plays the role of the cloud in our experiments, a few seconds to search POIs.
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