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Cited by 10Privacy-Preserving Technologies in DataRecommender Systems and TechniquesPrivacy, Security, and Data Protection

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Decentralized Graph Neural Network for Privacy-Preserving Recommendation

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Zheng, Xiaolin, Wang, Zhongyu, Chen, Chaochao, Qian, Jiashu, Yang, Yao·2023·Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
DOI 10.1145/3583780.3614834
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Abstract

Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework.

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  • Various privacy preservation methodologies, such as randomization, differential privacy, secure multiparty computation, and homomorphic encryption, can be applied to build decentralized PPRS.

    “To address these vulnerabilities, various privacy preservation methodologies, such as randomization, differential privacy, secure multiparty computation, and homomorphic encryption, can be applied to build decentralized PPRS [14, 129-132, 138, 139, 141].”

    From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [132]

  • RDP enables the addition of less noise, enhancing the utility of recommendations while maintaining equal or superior privacy protection compared to other DP variations.

    “This approach enables the addition of less noise, enhancing the utility of recommendations while maintaining equal or superior privacy protection compared to other DP variations [132].”

    From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [132]

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Metadata

Authors
Zheng, X., Wang, Z., Chen, C., Qian, J., & Yang, Y.
Affiliations
  • Zhejiang University (CN)
  • Zhejiang Lab (CN)
ORCID
Venue
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
Publisher
ACM
Published
2023-10-21
Pages
3494-3504
ISSN
2155-0751 (ISSN-L)
DOI
10.1145/3583780.3614834
OpenAlex
W4387848877
Cited by
10
References
1
Citations by year
2026: 2 · 2025: 4 · 2024: 3 · 2023: 1
FWCI
1.78
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Key R&D Program of China (2022YFB4501504, 2022YFB4501500)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
Keywords
privacy-preserving recommendation, local differential privacy, user preference modeling, communication efficiency

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