Open AccessPrivacy Preserving in Social Networks Using Combining Cuckoo Optimization Algorithm and Graph Clustering for Anonymization
Author(s) -
Mehdi Namdarzadegan,
Taleb Khafaei
Publication year - 2019
Publication title -
asian journal of research in computer science
Language(s) - English
Resource type - Journals
ISSN - 2581-8260
DOI - 10.9734/ajrcos/2019/v3i330092
Subject(s) - computer science , cluster analysis , anonymity , k anonymity , the internet , cuckoo search , data mining , publication , clustering coefficient , cuckoo , path (computing) , social network (sociolinguistics) , transitive relation , social media , algorithm , internet privacy , machine learning , world wide web , computer security , computer network , mathematics , zoology , particle swarm optimization , advertising , business , biology , combinatorics
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.