Learning without Seeing: Improving Predictive Models with Secure Federated AI

Access to quality, diverse training data is paramount to improving models and more accurate predictions; however, privacy laws like GDPR, internal compliance requirements and data silos increasingly prevent the aggregation of these assets. Emerging cryptographic methods enable training data to remain federated and secure, so models can be effectively built across institutional silos without ever exposing the underlying data. This talk will describe the underlying technology and methods, and present several use-cases where financial services companies were able to train and use privacy-compliant models across jurisdictions, departments and organizations.

Privacy-preserving Machine Learning in Finance More (good) data yields better models, but increasing consumer awareness, privacy regulations and proprietary barriers mitigate access to valuable feature sets and our ability to leverage them. The conundrum of computing data without exposing it can be addressed with emerging cryptographic methods such as Secure Multiparty Computation and Fully Homomorphic Encryption. Furthermore, this opens the opportunity to monetize analytics while maintaining data privacy, security, and scarcity value. This talk will discuss the basics of the technologies and various real applications in financial institutions including fraud detection, credit analysis, customer discovery and more. Dr. Jordan Brandt is the CEO and cofounder at Inpher, a data security company pioneering privacy-preserving machine learning. As a Technology Futurist, Jordan’s research and insight on cybersecurity, AI, robotics and 3D printing have been featured in print and live broadcast internationally on Bloomberg, CNBC, Forbes, Financial Times, Wired and other business and technology press. Jordan is the former CEO and cofounder of Horizontal Systems, acquired by Autodesk (Nasdaq: ADSK) in 2011. He went on to serve as the director of Autodesk’s investment fund, while also teaching and conducting research as a Consulting Professor of Engineering at Stanford University. Jordan completed his undergraduate work at the University of Kansas and his PhD in Building Technology at Harvard. In 2014 he was selected as one of Forbes ‘Next-Gen Innovators’.