Introduction to Differential Privacy In Machine Learning

As machine learning is going mainstream it's very important to protect privacy of the individuals whose data is being used during training. In this talk audience will learn the concept of differential privacy and how to build machine learning models using techniques which provide privacy guarantees. Furthermore this talk will review the current research and industrial scene on differential privacy.

 
 

Outline/Structure of the Talk

  • Introduction to differential privacy in machine learning
  • Techniques for building Machine learning models which guarantee privacy
  • Review of current state of art techniques to incorporate differential privacy in machine learning

Learning Outcome

  • Be aware of the concerns regarding privacy of training dataset and published machine learning models
  • Clear techniques for incorporating differential privacy while building machine learning models

Target Audience

Data Scientist, Machine Learning Engineers, Product Managers

schedule Submitted 7 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  6 months ago
    reply Reply

    Dear Amogh: Will you show a specific implementation for a specific use case with code or a walk-through? Will you primarily focus on the concepts only?

    Warm Regards

    Vikas

    • Dr. Vikas Agrawal
      By Dr. Vikas Agrawal  ~  7 months ago
      reply Reply

      Dear Amogh: Would you like to add a video of you presenting or introducing the topic please?

      Warm Regards

      Vikas