Efficiency Boosting of Secure Cross-platform Recommender Systems over Sparse Data. (arXiv:2212.01537v1 [cs.CR])

Fueled by its successful commercialization, the recommender system (RS) has
gained widespread attention. However, as the training data fed into the RS
models are often highly sensitive, it ultimately leads to severe privacy
concerns, especially when data are shared among different platforms. In this
paper, we follow the tune of existing works to investigate the problem of
secure sparse matrix multiplication for cross-platform RSs. Two fundamental
while critical issues are addressed: preserving the training data privacy and
breaking the data …

data efficiency platform recommender systems systems