DATA PRIVACY IN MACHINE LEARNING: PRINCIPLES, TECHNIQUES, AND CHALLENGES
Keywords:
Privacy-Preserving Machine Learning, Differential Privacy Optimization, Federated Learning Implementation, Privacy-by-Design Principles, Machine Learning SecurityAbstract
This article presents a comprehensive analysis of data privacy challenges and solutions in machine learning systems. Through examination of numerous privacy-enhanced ML implementations across various sectors, the article investigates the evolution, current state, and future directions of privacy-preserving techniques. The article reveals that while traditional privacy methods can significantly reduce model accuracy, advanced techniques like homomorphic encryption and secure multi-party computation achieve privacy preservation with minimal performance impact. The article demonstrates that organizations implementing privacy-by-design principles experience substantial reduction in privacy incidents, though with a modest increase in computational overhead. The article examines implementation challenges across different organizational scales, finding that while initial costs vary significantly between small and large enterprises, successful implementations achieve return on investment within a reasonable timeframe. The article also projects meaningful advancements in the coming years, including substantial improvements in algorithmic efficiency and significant reduction in computational overhead from current levels. These improvements suggest a future where privacy-preserving machine learning becomes increasingly feasible and cost-effective for organizations of all sizes.
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