Blog

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• Arbitrating the integrity of stochastic gradient descent with proof-of-learning

  by Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, and Nicolas Papernot

• Beyond federation: collaborating in ML with confidentiality and privacy

  by Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud and Nicolas Papernot

• Is this model mine?

  by Pratyush Maini, Mohammad Yaghini and Nicolas Papernot

• To guarantee privacy, focus on the algorithms, not the data

  by Aleksandar Nikolov and Nicolas Papernot

• Teaching Machines to Unlearn

  by Christopher A. Choquette-Choo, Varun Chandrasekaran, and Nicolas Papernot

• In Model Extraction, Don’t Just Ask ‘How?’: Ask ‘Why?’

  by Matthew Jagielski and Nicolas Papernot

• How to steal modern NLP systems with gibberish?

  by Kalpesh Krishna and Nicolas Papernot

• How to know when machine learning does not know

  by Nicolas Papernot and Nicholas Frosst

• Machine Learning with Differential Privacy in TensorFlow

  by Nicolas Papernot

• Privacy and machine learning: two unexpected allies?

  by Nicolas Papernot and Ian Goodfellow

• The challenge of verification and testing of machine learning

  by Ian Goodfellow and Nicolas Papernot

• Is attacking machine learning easier than defending it?

  by Ian Goodfellow and Nicolas Papernot

• Breaking things is easy

  by Nicolas Papernot and Ian Goodfellow