Research on Big Data Privacy Protection Mechanisms Based on Deep Learning and Federated Learning
DOI:
https://doi.org/10.53469/wjimt.2025.08(05).11Keywords:
Federated Learning, Differential Privacy, Data Security, Distributed Machine LearningAbstract
In the era of data-driven artificial intelligence, the contradiction between privacy protection and data value mining has become increasingly prominent. This paper systematically explores the technical system of privacy protection within the integrated framework of deep learning and federated learning. It analyzes the implementation mechanisms of core technologies such as differential privacy, homomorphic encryption, and secure multi-party computation, and reviews cutting-edge directions including optimization of non-independent and identically distributed (Non-IID) data and defense against adversarial attacks. By comparing application practices in typical scenarios such as healthcare and finance, the paper reveals the "impossible trinity" dilemma of privacy-utility-efficiency and proposes future breakthrough directions such as quantum-secure encryption and game-theoretic collaboration. Research indicates that the collaborative evolution of technological fusion innovation and legal-ethical constraints will be the key path to constructing a trustworthy artificial intelligence ecosystem.
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