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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
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Security and Privacy Issues in Deep Learning
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Wu, B., Zhao, S., Sun, G., Zhang, X., Su, Z., Zeng, C., & Liu, Z. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00220 · Cited by 44
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
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“It aggregates data based on class distribution while keeping user data private on devices.”
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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