Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

• DOI: 10.48550/arxiv.2202.07253
• Publication Date: 2022-01-01
• Authors: Jamie Cui; Chaochao Chen; Lingjuan Lyu; Carl Yang; Wang Li
• Keywords: Homomorphic Encryption; Benchmark (surveying)

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that all are available to the platform. However, in practice, user-item interaction (e.g.,rating) and user-user usually generated by different platforms, both of which contain sensitive information. Therefore, "How perform secure efficient across where highly-sparse nature" remains important challenge. In this work, we bring computation techniques into recommendation, propose S3Rec, a sparsity-aware cross-platform framework. As result, our model can not only improve performance rating platform incorporating sparse on platform, but also protect privacy platforms. Moreover, further training efficiency, two matrix multiplication protocols based homomorphic encryption private information retrieval. Our experiments benchmark datasets demonstrate effectiveness S3Rec.