The Privacy Bootstrap
提出一种通过自助法生成随机噪声来扰动微观数据的方法，在保护个体隐私的同时允许恢复总体分布，并用回归示例探讨了隐私保护与估计效率之间的权衡。
Methods for the privacy protection of microdata include grouping, deleting records or adding simulated records, data swapping, and the publication of data perturbed with random noise. We suggest a variant of the latter in which the noise is generated by bootstrapping from the original empirical distribution. The published data distribution then essentially consists of a convolution of a distribution with itself, and the distribution can be recovered, although the individual observations remain protected. By means of a regression example, we explore the trade-off between privacy protection based on bootstrapping and the efficiency of estimation using the published data. For reasonable loss measures, the trade-off is hyperbolic in character. Some encouraging simulation results are reported.