The following pages describe our experimental methodology as well as discuss basic concepts relevant to our findings.
Understanding scaled measures of error
Key characteristics of the data that affect the error introduced by a differentially private algorithm.
The scale of the data and the epsilon privacy parameter contribute to error in equivalent ways.
Baseline algorithms help to define the limits of reasonable utility.
Data-dependent algorithms perform differently on different datasets.