Probabilistic matrix factorization with personalized differential privacy

Volume: 183, Pages: 104864 - 104864
Published: Nov 1, 2019
Abstract
Probabilistic matrix factorization (PMF) plays a crucial role in recommendation systems. It requires a large amount of user data (such as user shopping records and movie ratings) to predict personal preferences, and thereby provides users high-quality recommendation services, which expose the risk of leakage of user privacy. Differential privacy, as a provable privacy protection framework, has been applied widely to recommendation systems. It is...
Paper Details
Title
Probabilistic matrix factorization with personalized differential privacy
Published Date
Nov 1, 2019
Volume
183
Pages
104864 - 104864
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