An augmented Location Privacy Optimization of mobile nodes using Hybrid ALO and RFO algorithm

Abstract

The location based services (LBS) helps the users to collect highly personalized data over the internet. In LBS, the information is supplied depending upon the user’s trajectory location. Here, data security issues are of great concern when location information of user node is being provided to a third party for LBS. Thus, it is one of the major challenges for LBS to safeguard the user’s privacy data from the network hackers. In this paper, a hybrid mechanism for identification of malicious users in location based network is proposed. In our system, the user’s location is protected by adding score function to k-anonymity algorithm for reducing the possibility of the requested user node position to be introduced to an unauthorized source. A cloaked region is produced by the anonymizer to shrink the quantity of data processed with isolating the user location so as to protect sensitive details. Initially, query issuer finds the shortest distance between the user nodes and transmit the same query to the nearest user nodes based on the k-anonymity level. The query is encrypted by additive homomorphic encryption algorithm which prevents the leakage of users’ private data during transmissions. For identifying a malicious user, a hybrid ALO-RFO algorithm is proposed. The efficacy of the proposed system is proved by analyzing the parameters such as packet delivery ratio, average delay, detection rate, accuracy with the existing algorithms. Hence, there by the comparative investigations made proves that hybrid ALO-RFO algorithm with score value attains improved performance when compared with existing techniques.