The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Differential privacy and robust statistics
Abstract
We show by means of several examples that robust statistical estimators present an excellent starting point for differentially private estimators. Our algorithms use a new paradigm for differentially private mechanisms, which we call Propose-Test-Release (PTR), and for which we give a formal definition and general composition theorems.
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Citations
Cites
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-citationHuber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
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
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
Self-citationHuang, 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
(2011) · PubMed · Cited by 990
A Hybrid Approach to Privacy-Preserving Federated Learning
Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., & Zhou, Y. (2019) · Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3338501.3357370 · Cited by 1,029
Metadata
- Authors
- Dwork, C., & Lei, J.
- Venue
- Proceedings of the forty-first annual ACM symposium on Theory of computing
- Publisher
- ACM
- Published
- 2009-05-31
- Pages
- 371-380
- OpenAlex
- W2138865266
- Cited by
- 611
- References
- 3
- FWCI
- 12.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Dwork, C., & Lei, J. (2009). Differential privacy and robust statistics. Proceedings of the forty-first annual ACM symposium on Theory of computing. https://doi.org/10.1145/1536414.1536466
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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.
