Visual utility evaluation of differentially private scatterplots
Differentially private scatterplots enable the plotting of two attributes while guaranteeing a specified level of privacy. What a user sees from the scatterplot can be affected by which privacy algorithm is used and how it adds noise to the data. However, there is no existing work that quantifies this effect. We present the results of a pilot data study that compares the visual utility of algorithms that create differentially private scatterplots. We compare five popular algorithms across a range of parameters. The results indicate that DAWA and Geometric Truncated are the best algorithms for visual utility. Future research could focus on optimizing the different parameters to maximize utility of the visual representations.