Investigating Visual Analysis of Differentially Private Data
Investigating Visual Analysis of Differentially Private Data
复制标题
调查不同私人数据的可视化分析
DOI：
10.1109/tvcg.2020.3030369
复制
发表时间：
2020-10
影响因子：
5.2
通讯作者：
Dan Zhang;Ali Sarvghad;G. Miklau
中科院分区：
文献类型：
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作者：
Dan Zhang;Ali Sarvghad;G. Miklau
差异隐私是一种新兴的隐私模型，在许多领域越来越受欢迎。它的作用是向数据中添加经过仔细校准的噪声，从而模糊有关个人的信息，同时保留有关人口的总体统计数据。从理论上讲，可以通过绘制差异化的隐私数据来生成强大的隐私保护可视化效果。然而，噪声引起的数据扰动可能会改变视觉模式并影响私人可视化的效用。我们对使用私有可视化进行可视化数据探索和分析的挑战和机遇仍然知之甚少。作为填补这一空白的第一步，我们进行了众包实验，针对八个分析任务和四种可视化类型（条形图、饼图、折线图、散点图）的组合，测量参与者在三个隐私级别（高、低、非隐私）下的表现。我们的研究结果表明，参与者对摘要任务（例如，在数据中查找聚类）的准确性高于价值任务（例如，检索某个值）。我们还发现，在 DP 下，饼图和折线图提供与条形图相似或更好的准确性。在这项工作中，我们贡献了实证研究的结果，研究了基本私有可视化的基于任务的有效性、用于定义和衡量用户在 DP 下执行视觉分析任务成功的二分模型，以及一组用于调整注入以提高私有可视化实用性的分布指标。
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.