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.

BLC: Private matrix factorization recommenders via automatic group learning / Checco, A.; Bianchi, G.; Leith, D. J.. - In: ACM TRANSACTIONS ON PRIVACY AND SECURITY. - ISSN 2471-2566. - 20:2(2017). [10.1145/3041760]

BLC: Private matrix factorization recommenders via automatic group learning

Checco A.;
2017

Abstract

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.
2017
Clustering; Matrix factorization; Privacy; Recommender systems
01 Pubblicazione su rivista::01a Articolo in rivista
BLC: Private matrix factorization recommenders via automatic group learning / Checco, A.; Bianchi, G.; Leith, D. J.. - In: ACM TRANSACTIONS ON PRIVACY AND SECURITY. - ISSN 2471-2566. - 20:2(2017). [10.1145/3041760]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1680040
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