Differentially Private Boxplots
Authors: Kelly Ramsay, Jairo Diaz-Rodriguez
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulations, we show that this boxplot performs similarly to a non-private boxplot, and it outperforms the naive boxplot. Additionally, we conduct a real data analysis of Airbnb listings, which shows that comparable analysis can be achieved through differentially private boxplot visualization. |
| Researcher Affiliation | Academia | 1Department of Mathematics and Statistics, York University, Toronto, Canada. Correspondence to: Kelly Ramsay <EMAIL>. |
| Pseudocode | Yes | The algorithm, which we call DPBoxplot, is summarized in Algorithm 3, see also, Figure 1. |
| Open Source Code | Yes | Code. Official implementation is available at https: //github.com/jairoadiazr/DPBoxplot. |
| Open Datasets | Yes | We analyze a dataset containing Airbnb listing prices and associated metrics within New York City (NYC) in 2019 (Kaggle, 2019). |
| Dataset Splits | No | The paper describes using the Airbnb dataset and filtering some data points: 'After removing listings priced above 500 US dollars (USD) and requiring minimum nights of stay fewer than 10, this dataset has n = 40738 observations and d = 4 explanatory variables of business interest.' However, it does not specify any explicit training, validation, or test dataset splits for reproducibility. |
| Hardware Specification | Yes | All simulations were conducted on a single CPU and did not require significant computational resources. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for libraries, frameworks, or programming languages used in the implementation or experiments. |
| Experiment Setup | Yes | For all algorithms, we set a = 50 and b = 50 and λn = n -1/4. We ran the same simulations with other values of λn and found it did not alter the conclusions of the study, see Appendix C.2. For the unbounded algorithm, β was set to the default value of 1.001 for both the naive boxplot and DPBoxplot. |