The Privacy Bootstrap

Roger J Bowden and Ah Boon Sim

Journal of Business & Economic Statistics, 1992, vol. 10, issue 3, 337-45

Abstract: Methods for privacy protection of microdata include grouping and publication of data perturbed with random noise. The authors 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. The authors 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.

Date: 1992
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