To solve these problems, we design a secure friend recommendation system based on the user behavior, called PRUB. This system aims at achieving fine-grained recommendation to friends who share common interests without exposing the actual user behavior.
To evaluate our system PRUB, we utilize anonymous data from a Chinese ISP, which records the user browsing behavior for 3 months.
Section 3 provides the system overview of PRUB. The secure matching protocol is discussed in Section 4.
Our recommendation system PRUB is built on the hybrid management.
The PRUB system is based on users browsing behavior to recommend related friends with similar behavior and activities.
PRUB improved these protocols by adding promise information and VS validation, which enhance the security of the protocol.
In this section, we present the performance evaluation of PRUB. We first show how the user uses the system.
In this paper, we presented a secure friend recommendation system PRUB based on user behavior which deploys hybrid management architecture, as a way of reducing the pressure on servers.