In the age of machine learning, a big challenge is the security and privacy of data. Getting quality data to train models has always been a major hurdle. This barrier is growing and consequently hindering the wide deployment of ML solutions due to concerns about privacy and data misuse. Several privacy-preserving ML technologies are currently being discussed, yet the “holy grail” in this field is to apply ML to encrypted data.
Alon Kaufman and Vinod Vaikuntanathan discuss the challenges and opportunities of machine learning on encrypted data and describe the state of the art in this space.
Alon Kaufman is the cofounder and CEO at Duality Technologies. Previously, he was RSA’s global director of data science and innovation, leading data science across the company’s full portfolio. Alon has over 20 years of experience in technology and innovation management in high-tech companies, dealing with various aspects of artificial intelligence. He holds a PhD in computational neuroscience and machine learning from the Hebrew University and an MBA from Tel Aviv University.
Vinod Vaikuntanathan is an associate professor of computer science within MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and a cofounder of Duality Technologies. His research focuses on lattice-based cryptography and the theory and practice of computing on encrypted data. Vinod holds a PhD in computer science from MIT, where he received the George M. Sprowls Award for the best computer science thesis. His teaching and research in cybersecurity was recently recognized with MIT’s Harold E. Edgerton Faculty Achievement Award, a Sloan Faculty Fellowship, a Microsoft Faculty Fellowship, and a DARPA Young Faculty Award.
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