Generating-Based Attacks to Online Social Networks

Tianchong Gao, Yucheng Bian, Feng Li, Agnideven Palanisamy Sundar

Abstract: Online social network (OSN) privacy leakage problem addresses more and more users’ concerns. Studying the problem from attackers’ view could tell us how to prevent further data leakage. Currently, attackers mainly focus on mapping identities between their background knowledge and the published data to collect useful information. However, it becomes difficult to find the global optimal mapping strategy because of the complexity of the OSN data. This article proposes a novel generating-based attack on OSN data, no longer restricted to mapping-based information collection. Generally, the proposed scheme learns OSN properties from the attackers’ background knowledge and employs the knowledge to fill the unknown area in the published data. The proposed scheme employs a generative adversarial network to ensure the similarity between the generated graph and the published data. The conditional information is also added in the generation process such that the generated graph is restricted to the conditions under attackers’ background knowledge. Experimental results show that the proposed scheme successfully infer private information with real-world OSN datasets.