We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of "hiding in the crowd" privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or nym) setting and the BLC algorithm for carrying out this matrix factorization in a privacy-enhanced manner. We demonstrate that the increased privacy does not come at the cost of reduced recommendation accuracy.
@article{arxiv.1509.05789,
title = {BLC: Private Matrix Factorization Recommenders via Automatic Group Learning},
author = {Alessandro Checco and Giuseppe Bianchi and Doug Leith},
journal= {arXiv preprint arXiv:1509.05789},
year = {2017}
}