TEE-based decentralized recommender systems: The raw data sharing redemption. (arXiv:2202.11655v1 [cs.DC])

Recommenders are central in many applications today. The most effective
recommendation schemes, such as those based on collaborative filtering (CF),
exploit similarities between user profiles to make recommendations, but
potentially expose private data. Federated learning and decentralized learning
systems address this by letting the data stay on user's machines to preserve
privacy: each user performs the training on local data and only the model
parameters are shared. However, sharing the model parameters across the network
may still yield privacy breaches. …

arxiv data decentralized recommender systems redemption systems