Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling

The author presents a privacy-preserving approach for sharing runtime metrics based on differential privacy and data synthesis to maintain performance prediction accuracy. The main thesis is that synthetic training data can be used effectively to preserve privacy while maintaining model accuracy.