Mathematically Proven Privacy for ML and AI in Healthcare


Ishaan Nerurkar, CEO


Healthcare companies stand as stewards of some of the most sensitive data today: patient outcomes, claims data, genomic information and more. This on-demand webinar covers a mathematically rigorous technology for supporting machine learning and AI development, while protecting sensitive data: differential privacy. This talk outlines the common problems to overcome in successfully protecting data privacy, why existing techniques are fundamentally flawed, and how differential privacy achieves data privacy while unlocking the underlying data value.

In this webinar you will learn:

  • The basic concepts of differential privacy – what it does and doesn’t do.
  • The aims of privacy-preserving analytics in general.
  • Map healthcare-specific use cases that are enabled if privacy of the underlying data can be assured.

Watch now to discover how differential privacy is the answer for maximizing data utility and data privacy within the healthcare industry.


Ishaan Nerurkar


Ishaan Nerurkar is the CEO of LeapYear, a platform for privacy-preserving machine learning on sensitive data. LeapYear enables the world’s largest financial institutions, healthcare, and technology companies to safely leverage, share, and generate insights on previously restricted information.

Please enter your details, and the on-demand webinar will be sent to your email.

This website stores cookies on your computer to improve your website experience and provide more customized services to you. Read the cookie policy here.

I accept