Effects of differential privacy techniques: Considerations for end users.

Abstract We study the effects of differentially private (DP) noise injection techniques in a survey data setting, using the release of cost of early care and education estimates from the National Survey of Early Care and Education as a motivating example. As an example of how DP noise injection affects statistician estimates, our analysis compares the relative performance of DP techniques in the context of releasing estimates of means, medians, and regression coefficients. The results show that for many statistics, basic DP techniques show good performance provided that the privacy budget does not need to be split over too many estimates. Throughout, we show that small decisions such as the number of bins in a histogram or the scaling of a variable in a regression equation can have sometimes dramatic effects on the end results. Because of this, it is important to develop DP techniques with an eye towards the most important aspects of the data for end users.