Online differentially private inference for linear regression model
针对流数据这种常见的大数据类型，提出一种计算高效的差分隐私方法，用于线性回归模型的在线更新与推断，并给出参数估计、协方差估计和置信区间，理论分析和数值实验验证了方法的有效性。
Abstract In the era of big data, data privacy has attracted increasing attention. Differential privacy is a state‐of‐the‐art framework for formal privacy guarantees. Many privacy‐preserving inference methods have been developed for releasing information from a wide range of data analyses in the differential privacy framework. However, differential privacy statistical inference methods for streaming data, which represent a common type of big data, are still lacking. In this paper, we propose a computationally efficient privacy‐preserving method for online updating and inference of linear regression models that is differentially private. We derive regression parameter estimates in the differential privacy framework, along with the covariance estimates based on which privacy‐preserving confidence intervals for the parameters are constructed. We provide theoretical support for the proposed differentially private method, and numerical results demonstrate the good performance of our approach.