Investigating Visual Analysis of Differentially Private Data
| Autor: | Ali Sarvghad, Gerome Miklau, Dan Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
education.field_of_study
Information retrieval Line chart business.industry Computer science Bar chart Population Pie chart 020207 software engineering 02 engineering and technology Computer Graphics and Computer-Aided Design law.invention Visualization Data visualization law 020204 information systems Scatter plot Signal Processing 0202 electrical engineering electronic engineering information engineering Task analysis Computer Vision and Pattern Recognition business education Software |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 27:1786-1796 |
| ISSN: | 2160-9306 1077-2626 |
| DOI: | 10.1109/tvcg.2020.3030369 |
| Popis: | Differential Privacy is an emerging privacy model with increasing popularity in many domains. It functions by adding carefully calibrated noise to data that blurs information about individuals while preserving overall statistics about the population. Theoretically, it is possible to produce robust privacy-preserving visualizations by plotting differentially private data. However, noise-induced data perturbations can alter visual patterns and impact the utility of a private visualization. We still know little about the challenges and opportunities for visual data exploration and analysis using private visualizations. As a first step towards filling this gap, we conducted a crowdsourced experiment, measuring participants' performance under three levels of privacy (high, low, non-private) for combinations of eight analysis tasks and four visualization types (bar chart, pie chart, line chart, scatter plot). Our findings show that for participants' accuracy for summary tasks (e.g., find clusters in data) was higher that value tasks (e.g., retrieve a certain value). We also found that under DP, pie chart and line chart offer similar or better accuracy than bar chart. In this work, we contribute the results of our empirical study, investigating the task-based effectiveness of basic private visualizations, a dichotomous model for defining and measuring user success in performing visual analysis tasks under DP, and a set of distribution metrics for tuning the injection to improve the utility of private visualizations. |
| Databáze: | OpenAIRE |
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