ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations
Open accessAbstract
User-based KNN recommender systems ( UserKNN ) utilize the rating data of a target user’s k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors, since the recommendations could expose the neighbors’ rating data to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors’ ratings, which unfortunately reduces the accuracy of UserKNN . In this work, we introduce ReuseKNN , a novel differentially private KNN-based recommender system. The main idea is to identify small but highly reusable neighborhoods so that (i) only a minimal set of users requires protection with differential privacy and (ii) most users do not need to be protected with differential privacy since they are only rarely exploited as neighbors. In our experiments on five diverse datasets, we make two key observations. Firstly, ReuseKNN requires significantly smaller neighborhoods and, thus, fewer neighbors need to be protected with differential privacy compared with traditional UserKNN . Secondly, despite the small neighborhoods, ReuseKNN outperforms UserKNN and a fully differentially private approach in terms of accuracy. Overall, ReuseKNN leads to significantly less privacy risk for users than in the case of UserKNN .
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To achieve privacy protection, centralized RS typically uses non-cryptographic techniques due to their ease of application and low computational complexity.
“To achieve privacy protection, centralized RS typically uses non-cryptographic techniques due to their ease of application and low computational complexity [8, 123-125].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [124]
Section 2.2 (page unavailable) says differential privacy has been used in collaborative-filtering recommender systems and that homomorphic encryption has high computational complexity. It does not say centralized recommender systems typically prefer non-cryptographic techniques, or that ease of application and low computational complexity explain such a preference. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIn a centralized setup, the system is a trusted server responsible for maintaining data privacy.
“In a centralized setup, the system is a trusted server responsible for maintaining data privacy [99, 123-126].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [124]
The Introduction says the recommender-system provider specifies which users need privacy protection, supporting the idea that the system operator has a role in protecting data. It does not describe a centralized setup or characterize the server as trusted; searches for “trusted server” and “centralized” found no such description in the paper’s discussion. (LLM, conf=0.78)
Experimental — accuracy not yet measuredDP noise can be introduced at various stages: on the input data, the model parameters, or the final recommendations.
“This carefully calibrated noise can be introduced at various stages: on the input data [14, 124, 138, 172], the model parameters [101, 171, 173], or the final recommendations [172].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [124]
Section 5.2.2 says ReuseKNN applies DP by adding noise to neighbors’ rating values, supporting the input-data part of the claim. Appendix B evaluates the resulting top-N recommendations, but does not say noise is added to the final recommendations themselves. The model-parameters clause cites other sources, not this paper. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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- Authors
- Müllner, P., Lex, E., Schedl, M., & Kowald, D.
- Affiliations
- Graz University of Technology (AT)
- Johannes Kepler University of Linz (AT)
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2023-08-11
- Volume
- 14
- Issue
- 5
- Pages
- 1-29
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W4384201236
- Cited by
- 27
- References
- 32
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 12 · 2023: 4
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- DDAI
- COMET Module within the COMET — Competence Centers for Excellent Technologies Programme
- Austrian Federal Ministry for Transport, Innovation and Technology
- Austrian Federal Ministry for Digital and Economic Affairs
- Austrian Research Promotion Agency
- TU Graz Open Access Publishing Fund, the Austrian Science Fund (P33526 and DFH-23)
- State of Upper Austria and the Federal Ministry of Education, Science, and Research (LIT-2020-9-SEE-113)
- 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
- differential privacy, k-nearest neighbors, recommender systems, user-based recommendation, privacy risks, privacy-utility trade-off
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