Exploring Privacy Preservation Mechanisms Security Frameworks and Ethical Governance Models for Responsible Data Mining in Large-Scale Distributed and Sensitive Information Systems
Keywords:
Privacy-Preserving Data Mining, Federated Learning, Secure Computation, Ethics in AI, Distributed Systems, Data Governance, Differential Privacy, Security FrameworksAbstract
The expansion of large-scale distributed data systems presents significant opportunities for organizations to leverage data mining for insights and decision-making. However, the challenge of handling sensitive data across decentralized environments necessitates robust privacy-preserving mechanisms, security frameworks, and ethically grounded governance. This study investigates the intersection of privacy, security, and ethics in responsible data mining. We analyze prevailing approaches such as k-anonymity, federated learning, and homomorphic encryption, and explore frameworks ensuring transparency and fairness. Drawing upon prior literature and comparative data, this paper outlines actionable principles to enable responsible data analytics in distributed and sensitive ecosystems.
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Copyright (c) 2025 Jean-François Boulicaut (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
