Epsilon Voting: Mechanism Design for Parameter Selection in Differential Privacy-Reference-Cited by-同舟云学术

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Epsilon Voting: Mechanism Design for Parameter Selection in Differential Privacy

Published:2018-09 Issue: Volume: Page:
ISSN:
Container-title:2018 IEEE Symposium on Privacy-Aware Computing (PAC)
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Author:

Kohli Nitin,Laskowski Paul

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8510985/8511818/08511827.pdf?arnumber=8511827

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. ULDP: A User-Centric Local Differential Privacy Optimization Method;2024 IEEE World AI IoT Congress (AIIoT);2024-05-29

2. Communicating the Privacy-Utility Trade-off: Supporting Informed Data Donation with Privacy Decision Interfaces for Differential Privacy;Proceedings of the ACM on Human-Computer Interaction;2024-04-17

3. Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis;Journal of Cybersecurity and Privacy;2023-12-01

4. Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile;Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security;2023-11-26

5. Centering Policy and Practice: Research Gaps Around Usable Differential Privacy;2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA);2023-11-01

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