**Scale:** Number of records in the input dataset.

**Shape:** aka Empirical Distribution. The proportions of the counts that reside in each bin.

**Domain Size:** Number of bins the domain is divided into.

**Input Dataset:** The input is represented as a histogram of counts. The domain is divided into bins of equal width.

**Output Dataset:**
The output is also represented as a histogram of counts over a uniform grid. The resolution of the grid matches that of the input. While the algorithms themselves may not actually generate a histogram, our visualization represents the histogram inferred from the noisy counts generated by the algorithm.

**Epsilon:** The privacy parameter. Controls how much information is disclosed about each record in the input dataset by a differentially private algorithm.

**Algorithm:** A differentially private algorithm. We consider algorithms that permit answering 1- and 2-dimensional range queries under differential privacy.

**Data Independent:** We call an algorithm data independent if the absolute error incurred by the algorithm is independent of the input dataset's shape and scale.