Fast Differentially Private Matrix FactorizationEI

    Cited by: 79|Bibtex|23|

    Conference on Recommender Systems, 2015.

    Abstract:

    Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a novel connection of differential privacy to Bayesian posterior sampling via Stochastic Gradient Langevin Dynamics. Due to...More
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