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Fueled by its successful commercialization, the recommender system (RS) hasgained widespread attention. However, as the training data fed into the RSmodels are often highly sensitive, it ultimately leads to severe privacyconcerns, especially when data are shared among different platforms. In thispaper, we follow the tune of existing works to investigate the problem ofsecure sparse matrix multiplication for cross-platform RSs. Two fundamentalwhile critical issues are addressed: preserving the training data privacy andbreaking the data silo problem. Specifically, we propose two concreteconstructions with significantly boosted efficiency. They are designed for thesparse location insensitive case and location sensitive case, respectively.State-of-the-art cryptography building blocks including homomorphic encryption(HE) and private information retrieval (PIR) are fused into our protocols withnon-trivial optimizations. As a result, our schemes can enjoy the HEacceleration technique without privacy trade-offs. We give formal securityproofs for the proposed schemes and conduct extensive experiments on both realand large-scale simulated datasets. Compared with state-of-the-art works, ourtwo schemes compress the running time roughly by 10* and 2.8*. They also attainup to 15* and 2.3* communication reduction without accuracy loss.

Efficiency Boosting of Secure Cross-platform Recommender Systems over Sparse Data