A Dummy Query-Based User Privacy Protection Scheme in Named Data Networking

Named Data Networking (NDN) can distribute content efficiently due to its characteristics of content naming and in-network caching, but these characteristics also raise privacy concerns. However, the existing NDN privacy protection schemes cannot protect user’s privacy completely because they neglect the correlation among NDN contents, content names and content caches. To solve this problem, this paper proposes a dummy query-based privacy protection scheme. Firstly, a similarity-based privacy metric applicable to NDN is formulated to measure the dispersion between queries, and the constraints of attacker’s background knowledge in NDN, communication overhead and NDN characteristics are established. Based on the above indicators, the two-step dummy query set selection algorithms are proposed to construct dummy query set. The algorithms select the dummies that satisfy the constraint on NDN distribution. From the selected dummies, the algorithms then filter out dummies that can be identified by taking account of decentralization, anonymity and communication overheads. Security analysis shows that our privacy protection scheme can effectively resist attacks against the user privacy in NDN. Furthermore, experimental results indicate that the proposal greatly improves user privacy compared with the existing schemes.