Differential Privacy-Preserving User Linkage across Online Social Networks

Many people maintain accounts at multiple online social networks (OSNs). Multi-OSN user linkage seeks to link the same person's web profiles and integrate his/her data across different OSNs. It has been widely recognized as the key enabler for many important network applications. User linkage is unfortunately accompanied by growing privacy concerns about real identity leakage and the disclosure of sensitive user attributes. This paper initiates the study on privacy-preserving user linkage across multiple OSNs. We consider a social data collector (SDC) which collects perturbed user data from multiple OSNs and then performs user linkage for commercial data applications. To ensure strong user privacy, we introduce two novel differential privacy notions, ϵ-attribute indistinguishability and ϵ-profile indistinguishability, which ensure that any two users' similar attributes and profiles cannot be distinguished after perturbation. We then present a novel Multivariate Laplace Mechanism (MLM) to achieve ϵ-attribute indistinguishability and ϵ-profile indistinguishability. We finally propose a novel differential privacy-preserving user linkage framework in which the SDC trains a classifier for user linkage across different OSNs. Extensive experimental studies based on three real datasets confirm the efficacy of our proposed framework.