Efficiently Calculating Stronger Lower Bound for Differentially Private SGD in Black-Box Setting
Author:
Affiliation:
1. NTT Social Informatics Laboratories,Tokyo,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10633276/10633246/10633308.pdf?arnumber=10633308
Reference24 articles.
1. Calibrating noise to sensitivity in private sata analysis;Dwork
2. Stochastic gradient descent with differentially private updates
3. Deep Learning with Differential Privacy
4. Auditing differentially pri-vate machine learning: How private is private sgd?;Jagielski;arXiv preprint,2020
5. Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
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