A Privacy-Preserving Scheme Based on Fragments Storage and Fragments Recombination in CCN

Content-Centric Networking (CCN) is one of the most important next-generation Internet architectures. The in network caching mechanism in CCN can bring higher efficiency and lower traffic to the network in terms of content distribution, but it also poses a great privacy risks. In this paper, we propose a privacy-preserving scheme based on fragments storage and fragments recombination (FS&FR) to solve the user's privacy leakage problem caused by timing attack in CCN. Firstly, the content in the network can be divided into different privacy levels according to the content provider, content consumer and router. Secondly, the optimal number of content fragments can be obtained by binary linear regression model based on content popularity, node betweenness and content privacy levels. Finally, the FS&FR algorithm is proposed and applied to the private content for content distribution and achieving fine-grained privacy protection. The simulation results show that the proposed scheme is secure yet highly efficient again timing attack compared to random-K delay algorithm. More specifically, the FS&FR algorithm can make the round-trip delays obtained by the attacker requesting the same content change, and then protect users' behavior privacy without sacrificing distribution performance.