Data science without seeing the data: A homomorphic encryption-based system
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
Tzvika Barenholtz works at Intuit’s data science organization and Intuit Futures, leading a team dedicated to advanced machine learning out of Intuit’s office in Israel. Previously, he lead product teams at Facebook, Google, and EMC.
Induprakas (Indu) Keri is the vice president of development at Intuit, with responsibility for driving the company’s blockchain and distributed ledger technology strategy. Indu has had a long and distinguished career with leading high technology companies. Previously, he was chief information security officer and vice president of cloud security at Intuit; was chief operating officer of Limelight, the number two content delivery network, where he was responsible for products, R&D, professional services, and customer support, and he built out a robust R&D organization, established people and process improvements that resulted in double-digit net promoter score increases, and presided over the largest capacity increase in the company’s history; was executive vice president products and chief technology officer at Sungard AS, where he drove the overall technology strategy for the company, led R&D, and delivered cloud-based disaster recovery products; was as an engineer at Silicon Graphics, where he implemented his dissertation work into the high-end parallelizing compiler delivered by SGI; and stints at McKinsey & Company, BEA Systems, and Oracle.
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