alexa Enhance Privacy Preserving In Location Based Service
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Special Issue Article

Enhance Privacy Preserving In Location Based Service

Karthik R1, Anguraj S2
  1. M.Tech, Dept of IT, K.S.R. College of Engineering, Tamilnadu, India
  2. Assistant Professor, Dept of IT, K.S.R. College of Engineering, Tamilnadu, India
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Abstract

Location Based Services (LBSs) have recently attracted much attention due to the advancementof GPS facilitates. In LBS, the private and confidential information of user may disclose to others since LBS need a user’s location. To protect the privacy of users, many cloaking algorithms have been proposed to hide user’s actual location. Here to improve the cloaking algorithm performance and location privacy.A fundamental approach to perform the class of k- Nearest Neighbor (k-NN) queries, the core class of queries used in many of the location-based services, without revealing the origin of the query in order to preserve the privacy of this information. The idea behind our approach is to utilize oneway transformations to map the space of all static and dynamic objects to another space and resolve the query blindly in the transformed space. However, in order to become a viable approach, the transformation used should be able to resolve k-NN queries in the transformed space accurately and more importantly prevent malicious use of transformed data by untrusted entities. Traditional encryption based techniques incur expensive O(n) computation cost (where n is the total number of points in space) and possibly logarithmic communication cost for resolving a K-NN[1] query. This is because such approaches treat points as vectors in space and do not exploit their spatial properties. In contrast, we use Hilbert curves as efficient one-way transformations and design algorithms to evaluate a K-NN query in the Hilbert transformed space. Consequently, we reduce the complexity of computing a K-NN query to and transferring the results to the client in O(K), respectively, where N, the Hilbert curve degree, is a small constant. Our results show that we very closely approximate the result set generated from performing KNN queries in the original space while enforcing our new location privacy metrics termed u-anonymity and a-anonymity, which are stronger and more generalized privacy measures than the commonly used K-anonymity and cloaked region size measures. AS a result, the security level of the proposed protocol is close to perfect secrecy without the aid of a trusted third party and simulation results show that the k- NN query accuracy rate of the proposed protocol is higher than 92% even when is large.

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