神经网络从一个样本中记住个人信息
John Hartley1, Pedro P Sanchez2, Fasih Haider2
1The University of Edinburgh, Edinburgh, UK. john.hartley@ed.ac.uk.
Scientific reports
|December 4, 2023
概括
深度神经网络 (DNN) 可以记住独特的患者特征,即使是从单个数据点,构成隐私风险. 研究人员开发了检测这种记忆的方法,而不需要原始的训练数据.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
背景情况:
- 深度神经网络 (DNN) 在大规模的疾病诊断中显示出高准确性.
- 人们对DNN模型中的数据隐私和算法偏差存在担忧.
- 患者数据隐私是医疗人工智能开发中的关键考虑因素.
研究的目的:
- 为了证明DNN可以从训练数据中记住独特的患者特征 (UF).
- 解释记忆现象,特别是对于罕见的概念.
- 提出检测DNN中UF记忆的方法.
主要方法:
- 分析DNN中唯一特征 (UF) 的记忆.
- 调查概念稀有性和记忆可能性之间的相关性.
- 开发和验证方法来识别UF记忆在没有访问培训数据的情况下.
主要成果:
- DNN可以记住UF,例如名称或ID,即使是从单个训练样本.
- 当 UF 代表数据集中的罕见概念时,记忆的可能性更大.
- 拟议的方法有效地识别DNN中的UF记忆,可供外部实体使用.
结论:
- 在DNN中UF的记忆对模型的稳定性和患者的隐私有影响.
- 通过记住的UF泄露患者数据的风险是显著的.
- 外部检测方法对于减轻部署的DNN中的隐私风险至关重要.
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