Data privacy preserving scheme in MANETs

Abstract

Data privacy is one among the challenging issues in Mobile Adhoc NETworks (MANETs), which are deployed in hostile environments to transfer sensitive data through multi-hop routing. The undesired disclosure of data can result in breach of data privacy, and can be used in launching several attacks. Many of the works achieved data privacy by using approaches such as data transformation, data perturbation, etc. But, these approaches introduce high computational overheads and delays in a MANET. To minimize the computations in preserving data privacy, we have proposed a computational intelligence based data privacy scheme. In the scheme we use data anonymization approach, where rough set theory is used to determine the data attributes to be anonymized. Dynamically changing multiple routes are established between a sender and a receiver, by selecting more than one trusted 1-hop neighbor nodes for data transfer in each routing step. Anonymity of the receiver is also discussed. The work has been simulated in different network sizes with several data transfers. The results are quite encouraging.