Data visualizations have been widely used on mobile devices like smartphones for various tasks (e.g., visualizing personal health and financial data), making it convenient for people to view such data anytime and anywhere. However, others nearby can also easily peek at the visualizations, resulting in personal data disclosure. In this paper, we propose a perception-driven approach to transform mobile data visualizations into privacy-preserving ones. Specifically, based on human visual perception, we develop a masking scheme to adjust the spatial frequency and luminance contrast of colored visualizations. The resulting visualization retains its original information in close proximity but reduces the visibility when viewed from a certain distance or further away. We conducted two user studies to inform the design of our approach (N=16) and systematically evaluate its performance (N=18), respectively. The results demonstrate the effectiveness of our approach in terms of privacy preservation for mobile data visualizations.

翻译：数据可视化已广泛应用于智能手机等移动设备，用于完成各类任务（如可视化个人健康和财务数据），使人们能够随时随地便捷地查看此类数据。然而，附近的人也能轻易窥视这些可视化内容，导致个人数据泄露。本文提出了一种基于感知驱动的方法，将移动数据可视化转化为隐私保护形式。具体而言，基于人类视觉感知原理，我们开发了一种掩蔽方案，用于调整彩色可视化的空间频率和亮度对比度。经处理后的可视化内容在近距离时保留原始信息，但一旦从一定距离外或更远处观看，其可见性便会降低。我们分别开展了两项用户研究：一项用于指导方法设计（N=16），另一项用于系统评估其性能（N=18）。结果表明，本方法在移动数据可视化的隐私保护方面具有显著效果。