Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization. (arXiv:2310.04015v3 [cs.LG] UPDATED)

While personalized recommendations systems have become increasingly popular,
ensuring user data protection remains a top concern in the development of these
learning systems. A common approach to enhancing privacy involves training
models using anonymous data rather than individual data. In this paper, we
explore a natural technique called \emph{look-alike clustering}, which involves
replacing sensitive features of individuals with the cluster's average values.
We provide a precise analysis of how training models using anonymous cluster
centers affects their generalization capabilities. We …

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