Differential Privacy

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Papers/Articles

  1. Differential Privacy: The Pursuit of Protections by Default (A discussion with Miguel Guevara, Damien Desfontaines, Jim Waldo, and Terry Coatta) – November 20, 2020 Volume 18, issue 5 (ACM queue) – Link
  2. Differentially Private SQL with Bounded User Contribution by Royce J Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel
    Simmons-Marengo, and Bryant Gipson – Link

Libraries/Tools

  1. OpenMined PySyft (A library for computing on data you do not own and cannot see) – Link
  2. Opacus (Train PyTorch models with Differential Privacy) – Link
  3. TensorFlow Privacy (Train TensorFlow models with Differential Privacy) – Link
  4. Google Differential Privacy – Link

Courses

  1. Secure and Private AI by Facebook Artificial Intelligence and Udacity – Link
  2. The Private AI Series by OpenMined – Link

Others

  1. OpenMined (OpenMined is an open-source community whose goal is to make the world more privacy-preserving by lowering the barrier-to-entry to private AI technologies.) – Link

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  1. Pingback: Dependable AI – Ranjan Kumar

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