Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing works assume that all data are available to the recommendation platform. However, in practice, user-item interaction data (e.g., rating) and user-user social data are usually generated by different platforms, both of which contain sensitive information. Therefore, How to perform secure and efficient social recommendation across different platforms, where the data are highly-sparse in nature remains an important challenge. In this work, we bring secure computation techniques into social recommendation, and propose S 3 Rec, a sparsity-aware secure cross-platform social recommendation framework. As a result, S 3 Rec can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms. Moreover, to further improve model training efficiency, we propose two secure sparse matrix multiplication protocols based on homomorphic encryption and private information retrieval. Our experiments on two benchmark datasets demonstrate that S 3 Rec improves the computation time and communication size of the state-of-the-art model by about 40× and 423× in average, respectively. How to perform secure and efficient social recommendation across different platforms, where the data are highly-sparse in nature? Specifically, we focus on the problem of collaborative social recommendation in the two-party model, where one party (denoted as P 0 ) is a rating platform that holds user-item rating data, and the other party (denoted as P 1 ) is a social platform that holds user-user social data. We also assume that the adversaries are semi-honest, which is commonly used in the secure computation literature 35th Conference on Neural Information Processing Systems (NeurIPS 2021).