Network Coding Based Privacy Preservation against Traffic Analysis in Multi-Hop Wireless Networks

Privacy threat is one of the critical issues in multi-hop 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 techniques, such as Onion Routing. In this paper, we propose a novel network coding based privacy-preserving scheme against traffic analysis in multi-hop wireless networks. With homomorphic encryption 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.