Machine Learning-based Online Social Network Privacy Preservation

Abstract

Online data privacy draws more and more concerns. Online Social Network (OSN) service providers employ anonymization mechanisms to preserve private information and data utility. However, these mechanisms mostly focus on the traditional definitions about privacy and utility. Recently, both benign data scientists and attackers utilize machine learning methods to extract information from OSNs. This paper aims to present a novel angle of balancing privacy and utility under machine learning. The proposed scheme perturbs the data that breaks the attackers' learning results and protect the benign third parties' learning results. To preserve both privacy and utility, we propose two different anonymization approaches to solve the multi-objective optimization problem. The first approach combines the two objectives. It utilizes the deep learning model, Generative Adversarial Network (GAN), to sequentially learns the two objectives and generates graphs. The second approach analyzes the differences between the two objects on structures. It utilizes Integrated Gradient (IG) in learning to break attackers' learning results. It structurally rewires edges to preserve third parties' learning results afterwards. The experiment results show that both approaches work well in privacy preservation.