Intuit is investing in adopting the new techniques of fully homomorphic encryption, which are just now moving from the theoretical and purely academic sphere into the realm of practitioners, a transition that will take many years. FHE means arithmetic operations can be calculated on the encrypted data, which opens up new possibilities for privacy-safe machine learning.
Tzvika Barenholz and Induprakas Keri detail some of the progress Intuit has made in solving the many technical problems that still plague FHE, including performance, support for different types of models and AI techniques, and the related system design.
What you'll learn
- Learn how Intuit is solving some of FHE's problems to bring it to more users
This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA.
- Title: Data science without seeing the data: Advanced encryption to the rescue
- Release date: February 2020
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920371212
You might also like
Designing Data-Intensive Applications
Data is at the center of many challenges in system design today. Difficult issues need to …
Data Science from Scratch, 2nd Edition
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, …
O'Reilly Strata Data Conference 2019 - New York, New York
The 2019 Strata Data Conference NYC, the biggest Big Data conference in the world, was a …
Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions …