Sensitivity-Aware Personalized Differential Privacy Guarantees for Online Social Networks

Abstract

With the prevalence of online social networks (OSNs), much personal information is collected and maintained by trusted service providers for third-party queries and analyses. Existing works regarding differentially private social network data publication overlook the fact that different users exhibit distinct privacy preferences or sensitivity inclinations. Neglecting these individual nuances may lead to privacy mechanisms that are overly conservative or inadequately protective. Furthermore, the injection of excessive noise into OSN data perceived by users as non-personal or less sensitive can incur additional privacy costs, resulting in lower service quality. This paper introduces a fine-grained, sensitivity-aware personalized edge differential privacy model (SPEDP) for OSNs. Specifically, SPEDP enables each OSN user to individually define the sensitivity level of their social connections, facilitating user-friendly personalized privacy settings. We design a privacy-aware mechanism that operates within a trusted service provider, capable of establishing privacy protection levels based on user-perceived sensitivity settings. Additionally, we propose a sensitivity-aware sampling mechanism to implement SPEDP. To further optimize the privacy mechanism, we explore a privacy threshold optimization strategy aimed at minimizing privacy budget waste. Finally, the personalized privacy protections and utility improvements achieved by the SPEDP mechanism are rigorously validated through theoretical analysis and comprehensive comparative experiments on benchmark datasets.