چكيده به لاتين
The usage of social networks shows a growing trend in recent years. Due to a large number of online social networking users, there is lots of data within these networks. Recently, advances in technology have made it possible to extract useful information about individuals and the interactions among them. In parallel, several methods and techniques were proposed to preserve users’ privacy through the anonymization of social network graphs. In this direction utilization of k-anonymity method, where k is the required threshold of structural anonymity, is among the most useful techniques. In this technique the nodes are clustered together to form the super-nodes of size at least k.
Our main contributions in this paper are, initially, to optimize the clustering process in k-anonymity method by means of the particle swarm optimization (PSO) algorithm in order to maximize the Normalized Structural Information Loss (NSIL). Although the proposed PSO based method shows a higher convergence rate than the previously introduced genetic algorithm (GA) based method, it did not provide a better NSIL value. Therefore, in order to achieve the NSIL value provided by GA optimization while preserving the high convergence rate obtained from PSO algorithm, we present hybrid solutions based on GA and PSO algorithms. Eventually, in order to achieve indistinguishable nodes, the edge generalization process is employed based on their relationships.
Simulation results demonstrate the efficiency of the proposed model to balance the maximize NSIL and algorithm's convergence rate.
Keywords: Social Network, k-Anonymity, Particle swarm optimization algorithm, Genetic algorithm, Structural Information Loss
