An example application scenario of our privacy-preserving visualization on a smartphone. The data owner (left side) in proximity can see the privacy-preserving visualization displayed on the smartphone, while the shoulder surfer (right side) at a far distance can not interpret the visualization content.


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 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.


Songheng Zhang, Dong Ma, Yong Wang. 2023. Don't Peek at My Chart: Privacy-preserving Visualization for Mobile Devices. Computer Graphics Forum (Proceedings of EuroVis 2023). To Appear.


Fastforward Video in EuroVis 2023

Presentation Video in EuroVis 2023


This research was supported by Lee Kong Chian Fellowship and the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 2 grant (Grant number: T2EP20222-0049). We would like to thank the participants in our user study and the anonymous reviewers for their valuable feedback.