Preserving Data Privacy During Data Transfer in MANETs

Abstract

Mobile adhoc networks consists of large number of mobile nodes, and is usually deployed to transfer data from a sender to a receiver using multi-hop routing. The data being transmitted may contain sensitive information, and undesired disclosure of information can lead to launching of various attacks, thus breaching the data privacy. Earlier works achieve data privacy by using approaches such as data transformation and data perturbation. However, these approaches introduce higher overheads and delays. We propose a computational intelligence based data privacy preserving scheme, where rough set theory is used to anonymize the data during data transfer. Data packets are enclosed within capsules that can be opened only by the designated node, thus preventing the undesired leakage of the data. Also, route between a sender and a receiver is changed dynamically by selecting more than one trusted 1-hop neighbor nodes in each routing step. The proposed data privacy preserving scheme is tested by considering different case studies in a MANET deployed for stock market. Theoretical analysis for data privacy is presented in terms of Information Gain by an attacker and Attacker Overhead, and the performance of proposed scheme against some of the attacks is also discussed. The simulation results show the effectiveness of proposed scheme.