Privacy Preserving Machine Learning and Data Governance for AI Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P121Keywords:
Preserving Machine Learning (PPML), Cryptographic Techniques, Decentralized Training Paradigms, Data GovernanceAbstract
As machine learning permeates sensitive domains such as healthcare, finance, and government, protecting individual privacy while leveraging large-scale data remains a paramount challenge. Privacy-Preserving Machine Learning (PPML) combines cryptographic techniques, decentralized training paradigms, and data governance policies to enable secure and compliant model development. This paper provides a comprehensive survey of fundamental PPML methods differential privacy, federated learning, homomorphic encryption and examines key data governance frameworks underpinning ethical AI adoption. We analyze technical trade-offs, including privacy-utility balance, scalability, and adversarial resilience. Finally, ongoing research directions and policy implications are discussed, emphasizing interdisciplinary collaboration for trustworthy AI deployment.
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