Helen: Maliciously Secure Coopetitive Learning for Linear Models
This addresses privacy and competition concerns for organizations like those in medical research or fraud detection, offering a maliciously secure solution for coopetitive learning.
The paper tackles the problem of collaborative training of linear models without sharing data, introducing Helen, a system that protects against malicious adversaries compromising m-1 out of m parties, achieving up to five orders of magnitude performance improvement over existing secure multi-party computation frameworks.
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 paper, we design and build 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.