"A General Framework for Auditing Differentially Private Machine Learning."

Fred Lu et al. (2022)

> Home

Details and statistics

DOI: —

access: open

type: Conference or Workshop Paper

metadata version: 2024-01-08

- view
  - electronic edition @ nips.cc (open access)
- export record
  dblp key:
  - conf/nips/LuMFLZRFT22
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/nips/LuMFLZRFT22
Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa:
A General Framework for Auditing Differentially Private Machine Learning. NeurIPS 2022

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.