Zhang, SonghengMa, DongWang, YongBujack, RoxanaArchambault, DanielSchreck, Tobias2023-06-102023-06-1020231467-8659https://doi.org/10.1111/cgf.14818https://diglib.eg.org:443/handle/10.1111/cgf14818Data 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 visibility when viewed from a certain distance or farther 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.CCS Concepts: Human-centered computing -> Information visualization; Security and privacy -> Privacy protectionsHuman centered computingInformation visualizationSecurity and privacyPrivacy protections10.1111/cgf.14818137-14812 pages