The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
Open accessAbstract
"Concentrated differential privacy" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations. We present an alternative formulation of the concept of concentrated differential privacy in terms of the Renyi divergence between the distributions obtained by running an algorithm on neighboring inputs. With this reformulation in hand, we prove sharper quantitative results, establish lower bounds, and raise a few new questions. We also unify this approach with approximate differential privacy by giving an appropriate definition of "approximate concentrated differential privacy."
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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
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
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
Mironov, I. (2017) · 2017 IEEE 30th Computer Security Foundations Symposium (CSF) · doi:10.1109/csf.2017.11 · Cited by 1,255
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
Self-citationDong, J., Roth, A., & Su, W. J. (2019) · arXiv · doi:10.48550/arxiv.1905.02383 · Cited by 389
SgxPectre: Stealing Intel Secrets from SGX Enclaves Via Speculative Execution
Chen, G., Chen, S., Xiao, Y., Zhang, Y., Lin, Z., & Lai, T. H. (2019) · 2019 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2019.00020 · Cited by 319
Metadata
- Authors
- Bun, M., & Steinke, T.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2016
- Pages
- 635-658
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2963699739
- Cited by
- 584
- References
- 2
- FWCI
- 16.16
- Type
- conference paper
- Language
- en
- Open access
- green
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- Bun, M., & Steinke, T. (2016). Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-662-53641-4_24
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