Abstrakt

Large Scale of Content Distribution Using Homomorphic Hash Function

Sridevi K, Senthil Nathan K

Privacy threat is one of the critical issues in wireless networks, where attacks such as traffic analysis and flow tracing can be easily launched by a malicious adversary due to the open wireless medium. Network coding has the potential to thwart these attacks since the coding/mixing operation is encouraged at intermediate nodes. However, the simple deployment of network coding cannot achieve the goal once enough packets are collected by the adversaries. On the other hand, the coding/mixing nature precludes the feasibility of employing the existing privacy-preserving technique. In this paper, we propose a novel network coding based privacy-preserving scheme against traffic analysis in wireless networks. In Homomorphic encryption, the proposed scheme offers two significant privacy-preserving features, packet flow untraceability and message content confidentiality, for efficiently thwarting the traffic analysis attacks. Moreover, the proposed scheme keeps the random coding feature. Theoretical analysis and simulative evaluation demonstrate the validity and efficiency of the proposed scheme.