On preserving privacy in content-oriented networks

The recent literature has hailed the benefits of content-oriented network architectures. However, such designs pose a threat to privacy by revealing a user's content requests. In this paper, we study how to ameliorate privacy in such designs. We present an approach that does not require any special infrastructure or shared secrets between the publishers and consumers of content. In lieu of any informational asymmetry, the approach leverages computational asymmetry by forcing the adversary to perform sizable computations to reconstruct each request. This approach does not provide ideal privacy, but makes it hard for an adversary to effectively monitor the content requests of a large number of users.