DS-ADMM++: A Novel Distributed Quantized ADMM to Speed up Differentially Private Matrix Factorization

Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent effici...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 33; no. 6; pp. 1289 - 1302
Main Authors: Zhang, Feng, Xue, Erkang, Guo, Ruixin, Qu, Guangzhi, Zhao, Gansen, Zomaya, Albert Y.
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
ADMM
Algorithms
Charge coupled devices
Costs
Differential privacy
Efficiency
Factorization
Machine learning
Matrix decomposition
Matrix factorization
parallel and distributed computing
Privacy
quantization
Quantization (signal)
Recommender systems
ISSN:1045-9219, 1558-2183
Online Access:Get full text
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Summary:Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent efficiency, to achieve the following two pieces of work: (1) Integrated local differential privacy paradigm into DS-ADMM to provide the privacy-preserving property; (2) Introduced a stochastic quantized function to reduce transmission overheads in ADMM to further improve efficiency. We named our work DS-ADMM++, in which one '+' refers to differential privacy, and the other '+' refers to quantized techniques. DS-ADMM++ is the first to perform efficient and private matrix factorization under the scenarios of differential privacy and DS-ADMM. We conducted experiments with benchmark data sets to demonstrate that our approach provides differential privacy and excellent scalability with a decent loss of accuracy.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2021.3110104