Privacy-Preserving Machine Learning for Autonomous Applications: Methodologies, Vulnerabilities, and Enterprise Governance

Abstract

ABSTRACT The deployment of autonomous multi-agent networks, intelligent connected vehicles, distributed smart grids, and localized mobile health devices relies heavily on collecting and processing massive streams of highly sensitive user telemetry data. Because standard centralized machine learning pipelines expose raw localized logs to significant privacy vulnerabilities—including data leakage, membership inference exploits, and model inversion attacks— the development of Privacy-Preserving Machine Learning (PPML) has become a paramount priority. This review provides a comprehensive technical, architectural, and mathematical evaluation of PPML paradigms optimized for decentralized autonomous applications. We critically analyze state-of-the-art cryptographic and statistical privacy frameworks, focusing explicitly on Federated Learning (FL), Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and Local Differential Privacy (LDP) primitives. The paper addresses a significant research gap: the absence of unified open-source validation testbeds capable of benchmarking computing latency profiles against privacy budget leakage thresholds over non-stationary physical channels. Finally, we establish a robust multi-tiered architectural governance model designed to safely implement privacy hardened distributed AI systems within modern cross-border regulatory compliance sectors.

KEYWORDS: Privacy-Preserving Machine Learning; Federated Learning; Differential Privacy; Homomorphic Encryption; Model Inversion Exploits; Privacy-Utility Trade-off.