Building trust/distrust relationships on signed social service network through privacy-aware link prediction process

With the ever-increasing popularity of social software, we can easily establish a signed social network (SSN) by capturing users' attitudes (i.e., trust/distrust, friend/enemies, consent/opposition) toward other people. However, the social relationships among users are often very sparse in an SSN, which impede the effective extension of the users' social circle significantly. To tackle this issue, researchers often use link prediction methods to search for missing links and predict new links in the network. However, existing link prediction methods cannot protect user's private information well. Considering this shortcoming, we propose a Simhash-based link prediction method with privacy-preservation. Concretely, we first apply Simhash to build less-sensitive user indices and then determine the probably similar friends (i.e., candidates) of a target user based on his or her indices. Through theoretical analysis, it can be known that the method proposed in this paper can effectively protect users' proprietary information. Second, for each candidate, we calculate his/her trust and distrust values with the target user. Third, we use Social Balance Theory to evaluate the possibility of building a link between the candidate and the target user based on the trust and distrust values. Finally, we conducted a set of experiments on the real-world Epinions dataset. Experimental results prove the advantages of our proposal in terms of overcoming the sparsity problem, compared to other competitive approaches. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a link prediction method using Simhash technology to protect user privacy, which has been proven through theoretical analysis and experimental validation to have advantages in overcoming the sparsity problem in social circle expansion.