A negative survey based privacy preservation method for topology of social networks

Abstract

With the development of social platforms, the social network has aroused wide attention. Since social networks contain a lot of personal sensitive information, many privacy preservation methods have been designed for social networks to allay concerns about privacy disclosure of people. However, most of the existing methods disturb the social networks too much to ensure the utility of social networks. To this end, we propose a negative survey based privacy preservation method, called NetNS, to preserve the topology privacy of social networks, where a dedicated negative survey model is developed to disturb edges in social networks in order to preserve the topology privacy of them. The theoretical analysis indicates that the developed NetNS is efficient, and can resist two common graph structure attacks including friendship attack and subgraph attack, while empirical studies conducted on three real-world social networks show that compared to six existing privacy preservation algorithms tailored for the topology of social networks, the developed NetNS can provide disturbed social networks with better utility while achieving same privacy preservation level.