Statistical Quantification of Differential Privacy: A Local Approach-Reference-Cited by-同舟云学术

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Statistical Quantification of Differential Privacy: A Local Approach

Published:2022-05 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Symposium on Security and Privacy (SP)
language:
Short-container-title:

Author:

Askin Onder¹,Kutta Tim¹,Dette Holger¹

Affiliation:

1. Ruhr-University Bochum

Funder

Deutsche Forschungsgemeinschaft

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9833550/9833558/09833689.pdf?arnumber=9833689

Reference39 articles.

1. Understanding the sparse vector technique for differential privacy

2. The algorithmic foundations of differential privacy;dwork;Found Trends Theor Comput Sci,2014

3. Extremal mechanisms for local differential privacy;kairouz;J Mach Learn Res,2016

Cited by 4 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. Eureka: A General Framework for Black-box Differential Privacy Estimators;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

2. Lower Bounds for Rényi Differential Privacy in a Black-Box Setting;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

3. DP-Discriminator: A Differential Privacy Evaluation Tool Based on GAN;Proceedings of the 21st ACM International Conference on Computing Frontiers;2024-05-07

4. DP-Opt: Identify High Differential Privacy Violation by Optimization;Wireless Algorithms, Systems, and Applications;2022

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