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:数据可视化已经被广泛用于移动设备上,如智能手机上的个人健康和财务数据可视化,使人们随时随地方便地查看这些数据。然而,附近的其他人也可以轻易地窥视可视化,导致个人数据泄露。在本文中,我们提出了一种感知驱动的方法,将移动数据可视化转化为隐私保护的数据可视化。特别是,基于人类视觉感知,我们开发了一种遮盖方案来调整彩色可视化的空间频率和亮度对比度。结果可视化保留了其原始信息的近距离视角,但从一定距离或更远处观看时,可视化的可见性降低。我们进行了两项用户研究,以指导我们方法的设计(N=16)和评估(N=18)其性能。结果证明了我们的方法在移动数据可视化的隐私保护方面的有效性。