Preserving Link Privacy in Uncertain Directed Social Graphs With Formal Guarantees

Data privacy breaches have prompted growing concerns regarding privacy issues on social networks. Preserving the privacy of links in the directed social graph, where edges signify the information flow or data contributions, poses a formidable challenge. However, existing methods for uncertain graphs primarily target undirected graphs and lack rigorous privacy guarantees. In this paper, we present a personal evidence protection algorithm called PEPA, which provides formally dual privacy guarantees for directed social links. Specifically, we implement out-link privacy to protect the out-links of nodes. Despite this protection, the exposure of in-links can still compromise privacy, potentially affecting service quality. To address this, we further introduce an uncertain directed graph algorithm as a post-processing approach for out-link privacy. This algorithm injects uncertainty into nodes’ in-links, effectively transforming the original directed graph into a probability-driven uncertain structure. Additionally, we propose an effective noise optimization method. Finally, we evaluate the trade-off between privacy and utility achieved by PEPA through comparative experiments. The results demonstrate privacy enhancements of PEPA compared to the $(k, \varepsilon )$-obfuscation algorithm and utility improvements over the RandWalk algorithm and UG-NDP. Particularly, PEPA demonstrates approximately a 2-fold improvement in utility compared to PEPA without noise optimization.