TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy
Pages 2153 - 2154
Abstract
Differential privacy is a rigorous mathematical model of privacy protection that has been the subject of deep theoretical research and also been deployed in real-world systems. This workshop aims to bring together a diverse array of researchers and practitioners to provoke stimulating discussion about the current state of differential privacy, in theory and practice. TPDP aims to be an inclusive forum that seeks to grow and diversify the differential privacy community.
References
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Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, and Zhiwei Steven Wu. 2020. Private Query Release Assisted by Public Data. In Proceedings of the 37th International Conference on Machine Learning (ICML '20).
[2]
Mark Bun, Roi Livni, and Shay Moran. 2020. An Equivalence Between Private Classification and Online Prediction. In Proceedings of the 61st Annual IEEE Symposium of Foundations of Computer Science (FOCS '20).
[3]
Marco Gaboardi, Michael Hay, and Salil Vadhan. 2020. A Programming Framework for OpenDP. (2020).
[4]
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, and Sergey Yekhanin. 2020. Differentially Private Set Union. In Proceedings of the 37th International Conference on Machine Learning (ICML '20).
[5]
Christina Ilvento. 2020 a. Implementing differentially private integer partitions. (2020). Working paper.
[6]
Christina Ilvento. 2020 b. Implementing sparse vector. (2020). Working paper.
[7]
Matthew Jagielski, Jonathan Ullman, and Alina Oprea. 2020. Auditing Differentially Private Machine Learning: How Private is Private SGD? (2020). arXiv pre-print 2006.07709.
[8]
Shuang Song, Om Thakkar, and Abhradeep Thakurta. 2020. Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems. (2020). arXiv pre-print 2006.06783.
[9]
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, and Steven Wu. 2020. Private Reinforcement Learning with PAC and Regret Guarantees. In Proceedings of the 37th International Conference on Machine Learning (ICML '20).
Index Terms
- TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy
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Information & Contributors
Information
Published In
October 2020
2180 pages
ISBN:9781450370899
DOI:10.1145/3372297
- General Chairs:
- Jay Ligatti,
- Xinming Ou,
- Program Chairs:
- Jonathan Katz,
- Giovanni Vigna
Copyright © 2020 Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 02 November 2020
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CCS '20
Sponsor:
CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security
November 9 - 13, 2020
Virtual Event, USA
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Overall Acceptance Rate 1,261 of 6,999 submissions, 18%
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CCS '24
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