Dimitar Jetchev, EPFL and Inpher
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. Jetchev is the CTO and cofounder of Inpher, Inc leading research and development efforts. He is also a Professor of Mathematics at EPFL, Switzerland, funded by the Swiss National Science Foundation. He heads a research group in mathematical cryptology working on various fundamental problems in number theory and mathematical cryptology. Dimitar has developed algorithms for a prominent HFT firm in New York, and as a Microsoft Research Fellow at the Cryptography and Anti-Piracy Group he has contributed to the design and development of the Windows software-licensing scheme. Dr. Jetchev holds a B.A. in mathematics from Harvard University and M.A. and Ph.D. from University of California, Berkeley.