Preservation of People's Privacy in Social Networks Based on Clustering-Based Honey Bee Optimization Algorithm
Mehdi Sadeghzadeh, Mohammad Reza Pourfasih · 2023
Nowadays, the use of social networks has developed widely.When people publish too much private information about themselves in these networks, their information may be attacked by an adversary, so there is a need to protect the privacy of people on these networks.One of the methods of preserving private information is k-anonymization.Anonymization is encountered with the challenge of data loss.A method is needed that ensures data anonymization while the utility is also well preserved.In this research, we try to create a proper model for data privacy and utility preservation by combining a graph cut clustering method and an artificial bee colony optimization algorithm.Two datasets are used in this research, which are Ca-GrQc including 5242 nodes and 14496 edges, and Polbooks containing 105 nodes and 441 edges.Three measures are used to evaluate this model, including, Transitivity, APL, and ACC.Finally, based results it can be declared proposed method, is a proper method for preserving privacy in social networks.