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Secured and Private AI Scholarship Challenge from Facebook

Objective

Train smarter AI models by learning to safely and securely use distributed private data with differential privacy, federated learning, and encrypted computation techniques.

Lessons

• Welcome to Scholarship Challenge
  • Welcome note to the course and challenge.
• Deep Learning with PyTorch
  • Hands on tutorials and introdutory notes to PyTorch Library.
• Introducing Differential Privacy
  • Basics of Differential Privacy, a method for measuring how operations impact the privacy of data.
• Evaluating the Privacy of a Function
  • Implementing Differential Privacy in Python.
• Introducing Local and Global Differential Privacy
  • Applying Differential Privacy to arbitrary algorithms by adding noise to the outputs.
• Differential Privacy for Deep Learning
  • Differential Privacy to Deep Neural Networks
• Federated Learning
  • Methods for preserving data privacy by training models where the data lives.
• Secruring Federated Learning
  • Secure models trained with multi-party computation.
• Encrypted Deep Learning
  • Performing encryted computation. Building an encrypted database, and generate an encrypted prediction with an encryted neural networkon on an encryted database.

My Badge

Course Completed on 29th August 2019