Fast Differentially Private Matrix Factorization
This addresses the problem of efficient and private recommendation systems for users and platforms, with incremental improvements in speed and implementation.
The paper tackles the challenge of achieving both accuracy and speed in differentially private collaborative filtering by presenting a simple algorithm that connects differential privacy to Bayesian posterior sampling via Stochastic Gradient Langevin Dynamics, resulting in the ability to generate 1024-dimensional models at a rate of 8.5 million recommendations per second on a single PC.
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 its simplicity the algorithm lends itself to efficient implementation. By careful systems design and by exploiting the power law behavior of the data to maximize CPU cache bandwidth we are able to generate 1024 dimensional models at a rate of 8.5 million recommendations per second on a single PC.