Privacy in content-centric networking against side channel attacks

Abstract

Content-centric network (CCN) is a new networking paradigm to overcome the shortcomings of today's internet. The central point in CCN is the named and addressable data or the content. Due to a few loopholes within the engineering of CCN, side-channel attacks are conceivable. In this paper, two such types of attacks are investigated, i.e., time-based and inference-based. We proposed an algorithm to preserve consumer privacy against time based attacks without much affecting the overall network utility. A comparative study of privacy vs. utility trade-off is presented. The algorithm is analysed both experimentally and theoretically and results are reported. We have also analysed both theoretically and experimentally, the probability and possible impact of frequency analysis based inference side-channel attack. A method is proposed to collect the auxiliary information needed to carry out the attack by probing the cache of the CCN routers. Possible countermeasures to mitigate the attack are also discussed.