Causal Discovery Under Local Privacy
局部隐私保护下的因果发现AI 解读 ↓
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局部隐私保护下的因果发现
- 研究问题
- 当数据提供者各自采用局部差分隐私机制时，噪声对变量相关性的扭曲如何影响因果结构学习，隐私与准确性之间如何权衡？
- 方法与思路
- 作者比较多种已有局部差分隐私机制，将因果学习算法应用于经机制扰动的数据，并评估隐私保护与所得因果结构准确性的关系。
- 材料陈述的贡献
- 摘要称比较结果能够帮助因果发现任务选择适当的局部隐私协议，提供隐私与结构恢复准确性权衡方面的经验认识。
- 阅读关注点
- 应核对隐私机制及预算、因果学习算法、图结构准确性指标，以及扰动是否保留算法要求的条件独立关系和其他识别前提。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列出具体机制、实验设定或比较结果，无法确定何种协议更适合何种数据。
原文摘要
Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled noise at the interface between the server that stores and processes the data, and the data consumers. Local differential privacy is a variant that allows data providers to apply the privatization mechanism themselves on their data individually. Therefore it provides protection also in contexts in which the server, or even the data collector, cannot be trusted. The introduction of noise, however, inevitably affects the utility of the data, particularly by distorting the correlations between individual data components. This distortion can prove detrimental to tasks such as causal discovery. In this paper, we consider various well-known locally differentially private mechanisms and compare the trade-off between the privacy they provide, and the accuracy of the causal structure produced by algorithms for causal learning when applied to data obfuscated by these mechanisms. Our analysis yields valuable insights for selecting appropriate local differentially private protocols for causal discovery tasks. We foresee that our findings will aid researchers and practitioners in conducting locally private causal discovery.