PRIV-ML: Analyzing Privacy Loss in Iterative Machine Learning with Differential Privacy

Abstract

Differential privacy offers rigorous protections for emerging paradigms like federated machine learning, decentralized analytics, and web3 applications. The parameters E (epsilon) and5)-differential privacy while sustaining utility with an average error of only 4.5% compared to non-private histograms, underscoring the importance of formally tracking cumulative privacy loss. Our framework provides a practical solution for measurable privacy-preserving machine learning pipelines without degrading accuracy or utility. By interfacing with diverse mechanisms and adapting noise to empirical sensitivities, we facilitate precise reasoning of privacy risks throughout model life cycles. We also analyze privacy parameter implications across application domains. This paper lays a rigorous foundation for developing trustworthy AI systems that protect sensitive data.