PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography

Understand

Privacy is a major issue in learning from distributed data.

• Recently the cryptographic literature has provided several tools for this task.
• However, these tools either reduce the quality/accuracy of the learning algorithm---e.g., by adding noise---or they incur a high performance penalty and/or involve trusting external authorities.
• We propose a methodology for {\sl private distributed machine learning from light-weight cryptography} (in short, PD-ML-Lite).