Uncertain Graph Method Based on Triadic Closure Improving Privacy Preserving in Social Network

Abstract

With the development and popularity of the Internet, various kinds of network services have gone deep into people's lives. Although the Internet brings convenience to people, it also brings us enormous information security problems. There may be a risk of privacy disclosure of individual privacy when the social network data is published. To mitigate these problems, we aim to improve privacy preserving in social networks by means of new methods. The purpose of this paper is to propose an efficient uncertain graph method based on the triadic closure, which includes adding edges, determining the triangle and injecting probability. In order to improve privacy preserving and data utility efficiently, we add nearby potential edges and keep the expected values unchanged. The experimental results show that the method not only improves the performance of privacy preserving in the social network, but also reduces the loss of data utility for the original graph.