Towards a More Reliable Privacy-preserving Recommender System

By Jiahui Jiang, Cheng-Te Li, Shou-de Lin. 2017

39 citations recorded by the source corpus.

This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether. That says, not only the ratings from the users to the items, but also the existence of the ratings as well as the learned recommendation model are kept private in our framework. Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the popular recommendation model, Matrix Factorization (MF). We further prove SDCF to meet the guarantee of Differential Privacy so that clients are allowed to specify arbitrary privacy levels. Experiments conducted on numerical rating prediction and one-class rating action prediction exhibit that SDCF does not sacrifice too much accuracy for privacy.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Eugenio Martínez-Cámara
2. M. V. Luzón
3. Daniel Jiménez-López
4. José Antonio Ruiz-Millán
5. Gerardo González-Seco
6. G. Stipcich
7. Francisco Herrera
8. M. Veganzones

Citations in the corpus

• Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy (2020, methodology): “Ultimately, several of these FL algorithms have been supplied with DP [27, 29, 30].”