Privacy Preserving in Social Networks Using Combining Cuckoo Optimization Algorithm and Graph Clustering for Anonymization

Abstract

Recently, social networks have received dramatic interest. The speed of the development and expansion of the Internet has created a new topic of research called social networks or online virtual communities on the Internet. Today, social networking sites such as Facebook, Twitter, Instagram and so forth are dramatically used by many people. Since people publish a lot of information about themselves on these networks, this information may be attacked by the intruders, so the need of preserving privacy is necessary on these networks. One of the approaches for preserving privacy is the K-anonymity. Anonymization always faces the challenge of data lost, therefore, an approach is required for anonymization of data and meanwhile maintaining the usefulness of the data. In this research, by combining the k-anonymity priority clustering method and Cuckoo optimization algorithm, an appropriate model is developed to maintain the privacy of the data and its usefulness. The average path length, average clustering coefficient and the transitivity criteria have been used to evaluate the proposed algorithm. The results of the experiments show that the proposed method in most cases has 1 unit superiority in terms of k-anonymity and 2 units superiority in terms of usefulness in comparison with similar methods.

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

“…Some research recommends the DP concept be used in recommendation in SN applications [43], high-dimensional PPDP [44], frequent sequence pattern mining without degrading user's privacy [45], and privacy preserving collaborative filtering [46]. Researchers have extended the traditional anonymization concepts for anonymizing SN users' graph G by either guaranteeing k-degree, k-edges, vertex and edge modification, clustering, and ℓdiverse sensitive node label retention [17,[47][48][49]. Despite the success of these privacy techniques, in most cases, either individual user private information is inferred through the community's detection, group membership, and friendship information, or users' community privacy breached if a group of users largely share the same SA value.…”