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：数据可视化已在智能手机等移动设备上广泛应用于各类任务（如展示个人健康和财务数据），极大方便人们随时随地查看数据。然而，附近的人也能轻易偷窥这些可视化内容，导致个人数据泄露。本文提出一种感知驱动方法，将移动数据可视化转换为隐私保护形式。具体而言，基于人类视觉感知特性，我们开发了一种掩蔽方案来调节彩色可视化的空间频率和亮度对比度。转换后的可视化在近距离仍保留原始信息，但当从一定距离或更远处观看时，其可见性会降低。我们分别开展了两项用户研究：一项用于指导我们的设计方法（N=16），另一项系统评估其性能（N=18）。结果表明，该方法在移动数据可视化的隐私保护方面具有显著效果。