research-article
Authors: Hanyang Liu, Yong Wang, Zhiqiang Zhang, Jiangzhou Deng, Chao Chen, and Leo Yu Zhang
Volume 61, Issue 4
Published: 18 July 2024 Publication History
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Abstract
Matrix factorization (MF) is an effective technique in recommendation systems. Since MF needs to utilize and analyze large amounts of user data during the recommendation process, this may lead to the leakage of personal data. Most of the current privacy-preserving MF research aims to protect explicit feedback, but ignores the protection of implicit feedback. In response to this limitation, we propose an adaptive differentially private MF (ADPMF) for implicit feedback. The proposed model is trained under the framework of Bayesian personalized ranking and uses gradient perturbation to achieve the ( ϵ, δ )-differential privacy. In our model, we design two effective methods, adaptive clipping and adaptive noise scale, to improve recommendation performance while maintaining privacy. We use Gaussian Differential Privacy (GDP) to accommodate privacy analysis for dynamically changing clipping thresholds and noise scale. Theoretical analysis and experimental results demonstrate that ADPMF not only achieves highly accurate recommendations but also provides differential privacy protection for implicit feedback. The results show that ADPMF can improve the recommended performance substantially by 10% to 20% compared to the current privacy-preserving recommendation methods and has promising application prospects in various fields.
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Highlights
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Bayesian personalized ranking is introduced to recommend by using implicit feedback.
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Gaussian Differential Privacy is used to ensure the privacy of implicit feedback.
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Adaptive gradient clipping is designed to improve the model performance.
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Adaptive noise scale decay is designed to improve the model performance.
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Our model improves the recommendation performance while ensuring privacy.
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Published In
Information Processing and Management: an International Journal Volume 61, Issue 4
Jul 2024
1167 pages
ISSN:0306-4573
Issue’s Table of Contents
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 18 July 2024
Author Tags
- Matrix factorization
- Gaussian differential privacy
- Implicit feedback
- Adaptive clipping
- Adaptive noise scale
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