PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers

Understand

Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations.

• Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem.
• However, existing cryptographic protocols still incur excess computational overhead.
• Here, we make a novel observation that this is partially due to naive adoption of mainstream numerical optimization (e.g., Newton method) and failing to tailor for secure computing.