General Graph Data De-Anonymization: From Mobility Traces to Social Networks

General Graph Data De-Anonymization: From Mobility Traces to Social Networks SHOULING JI and WEIQING LI, Georgia Institute of Technology MUDHAKAR SRIVATSA, IBM T. J. Watson Research Center JING SELENA HE, Kennesaw State University RAHEEM BEYAH, Georgia Institute of Technology When people utilize social applications and services, their privacy suffers a potential serious threat. In this article, we present a novel, robust, and effective de-anonymization attack to mobility trace data and social data. First, we design a Unified Similarity (US) measurement, which takes account of local and global structural characteristics of data, information obtained from auxiliary data, and knowledge inherited from ongoing de-anonymization results. By analyzing the measurement on real datasets, we find that some data can potentially be de-anonymized accurately and the other can be de-anonymized in a coarse granularity. Utilizing this property, we present a US-based De-Anonymization (DA) framework, which iteratively deanonymizes data with accuracy guarantee. Then, to de-anonymize large-scale data without knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. By smartly working on two core matching subgraphs, ADA achieves high de-anonymization accuracy and reduces computational overhead. Finally, we examine the presented de-anonymization attack on three well-known mobility traces: St Andrews, Infocom06, and Smallblue, and three social datasets: ArnetMiner, Google+, and Facebook. The experimental results demonstrate that the presented de-anonymization framework is very effective and robust to noise. The source code and employed datasets are now publicly available at SecGraph [2015]. CCS Concepts: Security and privacy Pseudonymity, anonymity and untraceability; and privacy Data anonymization and sanitization Additional Key Words and Phrases: Graph de-anonymization, social networks, mobility...