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Privacy Amplification for Episodic Training Methods...

by Vandy J Tombs, Olivera Kotevska, Steven R Young
Publication Type
Conference Paper
Book Title
Proceedings of the CIKM 2022 Workshops
Publication Date
Conference Name
International Workshop on Privacy Algorithms in Systems: PAS Workshop at CIKM'22
Conference Location
Atlanta, Georgia, United States of America
Conference Sponsor
Conference Date

It has been shown that differential privacy bounds improve when subsampling within a randomized mechanism. Episodic training, utilized in many standard machine learning techniques, uses a multistage subsampling procedure which has not been previously analyzed for privacy bound amplification. In this paper, we focus on improving the calculation of privacy bounds in episodic training by thoroughly analyzing privacy amplification due to subsampling with a multi-stage subsampling procedure. The newly developed bound can be incorporated into existing privacy accounting methods.