Privacy-Preserving and Efficient Friend Recommendation in Social Networks
Project Members
Bharath K. Samanthula, Lei Cen, Wei Jiang, Luo Si
Bharath K. Samanthula, Lei Cen, Wei Jiang, Luo Si
Abstract
Friend recommendation is a well-known application in many social networks and has been studied extensively in the recent past.
However, with the growing concerns about users’ privacy, there is a strong need to develop privacy preserving friend recommendation methods for social networks. In this paper, we propose two novel methods to recommend friends for a given user by using the common neighbors proximity measure in a privacy preserving manner. The first method is based on the properties of additive homomorphic encryption scheme and also utilizes a universal hash function for efficiency purpose. The second method utilizes the concept of protecting the source privacy through randomizing the
message passing path and recommends friends accurately. In addition, we empirically compare the efficiency and accuracy of the two methods. The proposed protocols act as a trade-off among security,
accuracy, and efficiency; thus, users can choose between these two
protocols depending on the application requirements.