Achieving Content-Oriented Anonymity with CRISP

As a popular realization of Information-Centric Network (ICN), Named Data Networking (NDN) greatly improves the efficiency of Internet content-distribution. A feature of NDN is that it improves privacy, as no addresses are needed for either the content consumer or publisher. However, NDN packets contain content names, and hence a well-motivated adversary can still deduce
what
content the user is requesting once it can link the packets and users. How to provide privacy in NDN, given its unique data retrieval mode, is an open problem. In this paper, we explore a specific content-oriented anonymity model called
content-user unlinkability
, which breaks the relationship between the content and the requesting user. We argue that achieving content-user unlinkability efficiently is a non-trivial task, since existing tunnel-based approaches will largely dismiss content caching of NDN, resulting in large content retrieval delay. To this end, we propose CRISP, namely
C
ooperative
R
andom
I
ntere
S
t
P
ropagation. In CRISP, routers cooperate to form full-meshed groups, within which content requests are randomly propagated before they are forwarded to content producers. We show CRISP can achieve probable content-user unlinkability with probabilistic models. Extensive simulations demonstrate that CRISP outperforms existing solutions including ANDANA and Crowds, in terms of both content retrieval latency and data throughput.