An Approach to Prevent Neighborhood Attack over Social Media

Abstract

Social media sites contain the personal information of the users, which entice the attackers The attacker uses several types of attacks on the social networking site to obtain sensitive information from the users. As different types of passive and active attacks are carried out on social media sites, user privacy may be compromised; to avoid this, the network operator releases data anonymously. Data from social media users is gathered and stored by social media operators for distribution to various third-party consumers. Because the fetched data frequently contains sensitive information, the network operator makes the entire graph available in anonymized and sanitized forms. It does not, however, provide a complete guarantee of user privacy. This research provided a way for anonymizing social network graphs using a neighborhood adjacency matrix-based anonymization process. This anonymization procedure could be utilized to defend against the social network graph's neighborhood attack. By adding fake edges to the social network graph, the suggested anonymization procedure increases the number of isomorphic neighborhood networks. As a result, a user's unique neighborhood network cannot be used to re-identify them in a social network graph.