  Information network analysis has drawn a lot attention in recent years. Among
all the aspects of network analysis, similarity measure of nodes has been shown
useful in many applications, such as clustering, link prediction and community
identification, to name a few. As linkage data in a large network is inherently
sparse, it is noted that collecting more data can improve the quality of
similarity measure. This gives different parties a motivation to cooperate. In
this paper, we address the problem of link-based similarity measure of nodes in
an information network distributed over different parties. Concerning the data
privacy, we propose a privacy-preserving SimRank protocol based on
fully-homomorphic encryption to provide cryptographic protection for the links.
