Helen: Maliciously Secure Coopetitive Learning for Linear Models
Event details
| Date | 20.06.2019 |
| Hour | 10:15 |
| Speaker | Prof. Raluca Popa |
| Location | |
| Category | Conferences - Seminars |
Abstract
Many organizations wish to collaboratively train machine learning models on their combined datasets for a common benefit (e.g., better medical research, or fraud detection). However, they often cannot share their plaintext datasets due to privacy concerns and/or business competition. In this talk, I will present Helen, a system that allows multiple parties to train a linear model without revealing their data, a setting we call coopetitive learning. Compared to prior secure training systems, Helen protects against a much stronger adversary who is malicious and can compromise m − 1 out of m parties. Our evaluation shows that Helen can achieve up to five orders of magnitude of performance improvement when compared to training using an existing state-of-the-art secure multi-party computation framework.
Joint work with Wenting Zheng, Joey Gonzalez and Ion Stoica.
Biography
Raluca Ada Popa is an assistant professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab at UC Berkeley, as well as a co-founder and CTO of a cybersecurity startup called PreVeil. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of a Sloan Foundation Fellowship award, Microsoft Research Faculty Fellowship, Hellman Faculty Award, an Intel Early Career Faculty Honor award, George M. Sprowls Award for best MIT CS doctoral thesis, and a Johnson award for best CS Masters of Engineering thesis from MIT.
Many organizations wish to collaboratively train machine learning models on their combined datasets for a common benefit (e.g., better medical research, or fraud detection). However, they often cannot share their plaintext datasets due to privacy concerns and/or business competition. In this talk, I will present Helen, a system that allows multiple parties to train a linear model without revealing their data, a setting we call coopetitive learning. Compared to prior secure training systems, Helen protects against a much stronger adversary who is malicious and can compromise m − 1 out of m parties. Our evaluation shows that Helen can achieve up to five orders of magnitude of performance improvement when compared to training using an existing state-of-the-art secure multi-party computation framework.
Joint work with Wenting Zheng, Joey Gonzalez and Ion Stoica.
Biography
Raluca Ada Popa is an assistant professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab at UC Berkeley, as well as a co-founder and CTO of a cybersecurity startup called PreVeil. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of a Sloan Foundation Fellowship award, Microsoft Research Faculty Fellowship, Hellman Faculty Award, an Intel Early Career Faculty Honor award, George M. Sprowls Award for best MIT CS doctoral thesis, and a Johnson award for best CS Masters of Engineering thesis from MIT.
Practical information
- General public
- Free
Organizer
- Prof. Bryan Ford
Contact
- Margaret Escandari