An Efficient Privacy-Preserving Scheme against Traffic Analysis Attacks in Network Coding

Abstract

Privacy threat is one of the critical issues in network coding, where attacks such as traffic analysis can be easily launched by a malicious adversary once enough encoded packets are collected. Furthermore, the encoding/mixing nature of network coding precludes the feasibility of employing the existing privacy-preserving techniques, such as Onion routing, in network coding enabled networks. In this paper, we propose a novel privacy-preserving scheme against traffic analysis in network coding. With homomorphic encryption operation on global encoding vectors (GEVs), 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, and each sink can recover the source packets by inverting the GEVs with a very high probability. Theoretical analysis and simulative evaluation demonstrate the validity and efficiency of the proposed scheme.