基于医疗数据的人工智能训练隐私保护技术研究

Research on Privacy Protection Technology for Artificial Intelligence Training Based on Medical Data

摘要: 本文首先构建包含数据维度、技术维度与管理维度的多维度风险评估体系，通过量化指标明确训练数据全生命周期的隐私泄露隐患；其次系统梳理k-匿名、差分隐私、联邦学习等主流匿名化处理技术的原理与应用场景，结合医疗行业实验数据对比不同技术的性能优劣；最后从技术融合、标准构建与监管协同三个层面提出优化路径，为人工智能训练数据的隐私保护提供理论支撑与实践参考。

Abstract: This paper first constructs a multi-dimensional risk assessment system that includes data dimensions, technical dimensions, and management dimensions, and clarifies the privacy leakage risks throughout the entire life cycle of training data through quantitative indicators. Secondly, it systematically sort out the principles and application scenarios of mainstream anonymization processing technologies such as k-anonymity, differential privacy, and federated learning, and compare the performance advantages and disadvantages of different technologies in combination with the experimental data of the medical industry; Finally, optimization paths are proposed from three aspects: technology integration, standard construction, and regulatory collaboration, providing theoretical support and practical reference for the privacy protection of artificial intelligence training data.