LDP based Social Content Protection for Trending Topic Recommendation

Trending topic recommendation (TTR) has become a popular social service for social users to obtain interesting topics based on public social content. Due to sensitive privacy, the traditional differential privacy (DP) methods are used to protect social contents. However, these DP methods rely on a fully trusted third party (TTP) without considering protecting the correlations of social keywords, and do not support the privacy-preserving online social content publication. In this article, we propose a novel local DP-based TTR (LDPTR) scheme to perform high-quality online TTR services while achieving local privacy-preserving social contents. Specifically, LDPTR first clusters the social keywords with high correlations into noisy graph classes based on a graph-based LDP (GLDP) algorithm for ensuring the keyword correlation privacy. Second, LDPTR adopts a novel mechanism called ∈2-compressive sensing indistinguishability (CSI) to generate noisy social topics, thereby preventing the user-linkage attack and breaking the curse of high-dimensional local differential privacy (LDP). Then, a dynamic graph-based CSI (DGCSI) algorithm is proposed to protect the online social content privacy while ensuring data usability. Furthermore, LDPTR calculates the trending topics of interest with high burstiness by using a topic distribution similarity model-based topic burstiness (TMTB) algorithm, which achieves effective TTR services. Our LDPTR scheme satisfies ∈-LDP by detailed security analysis. Extensive experiments over two real-world data sets show that the proposed LDPTR gets high data utility while ensuring high-level privacy.