Interpreting the Error of Differentially Private Median Queries through Randomization Intervals
This work addresses a specific issue for practitioners in differential privacy by improving the utility of median queries without sacrificing interpretability, representing an incremental advancement in the field.
The paper tackled the problem of interpreting error in differentially private median queries by proposing PostRI, a method to compute randomization intervals after median estimation, which achieved 14%-850% higher utility compared to prior work while maintaining narrow intervals.
It can be difficult for practitioners to interpret the quality of differentially private (DP) statistics due to the added noise. One method to help analysts understand the amount of error introduced by DP is to return a Randomization Interval (RI), along with the statistic. A RI is a type of confidence interval that bounds the error introduced by DP. For queries where the noise distribution depends on the input, such as the median, prior work degrades the quality of the median itself to obtain a high-quality RI. In this work, we propose PostRI, a solution to compute a RI after the median has been estimated. PostRI enables a median estimation with 14%-850% higher utility than related work, while maintaining a narrow RI.