The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Differentially private recommender systems: Building privacy into the Netflix Prize contenders
Abstract
We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users. Specifically, we consider the Netflix Prize data set, and its leading algorithms, adapted to the framework of differential privacy.
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Cites
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
Self-citationDifferential Privacy: A Survey of Results
Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542
Robust De-anonymization of Large Sparse Datasets
Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372
Our Data, Ourselves: Privacy Via Distributed Noise Generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11761679_29 · Cited by 1,835
Self-citation
Cited by
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
Self-citationIntroduction to Recommender Systems Handbook
Ricci, F., Rokach, L., & Shapira, B. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_1 · Cited by 2,378
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
Certified Robustness to Adversarial Examples with Differential Privacy
Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., & Jana, S. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00044 · Cited by 750
Towards Making Systems Forget with Machine Unlearning
Cao, Y., & Yang, J. (2015) · 2015 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2015.35 · Cited by 622
Metadata
- Authors
- McSherry, F., & Mironov, I.
- Venue
- Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2009-06-28
- Pages
- 627-636
- OpenAlex
- W2022097286
- Cited by
- 664
- References
- 5
- FWCI
- 27.03
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
