Privacy-preserving analytics can be applied to any case in which multiple parties have private data that needs to be combined and analyzed without exposing the underlying data or machine learning models between the parties. The technology could be applied to preventing fraud in financial services, gaining new business insights in retail, or diagnosing health conditions using artificial intelligence. The most valuable data including financial transactions, customer data, and patient medical records are protected by laws that make it challenging for data scientists to extract value from the data without violating applicable privacy regulations.
Using Confidential Computing, organizations can now ensure that data and intellectual property contained in AI algorithms are both protected against tampering and compromise, including against insider threats within the partnering organizations or infrastructure providers. The data can be combined and analyzed within a secure enclave, with the output of encrypted results being returned to each party. Data remains cryptographically secure throughout the entire process, protecting the privacy of the data at rest, in transit, and in use.
In this webinar, Bob Rogers, Expert in Residence for AI to UCSF, and Richard Searle of Fortanix will discuss how to implement privacy-preserving analytics using confidential computing to protect private data. Bob Rogers will discuss how Fortanix is providing confidential computing to secure data and applications within the UCSF Center for Digital Health BeeKeeper AI project, which will accelerate the development of clinical healthcare AI algorithms using regulated data. Nehal Bandi will demonstrate how to use containerized AI applications in secure enclaves using Fortanix solutions.
WHAT WILL YOU LEARN
The webinar will take place live on Wednesday, October 28th, 2020 at 8:30AM Pacific Time / 3:30PM GMT and will be recorded for on-demand viewing. If you would like to attend live or received a link to the recording after the event, please register now.