Don’t peek at my chart: Privacy-preserving visualization for mobile devices
Publication Type
Journal Article
Publication Date
6-2023
Abstract
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.
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Publication
Computer Graphics Forum
Volume
42
Issue
3
First Page
137
Last Page
148
ISSN
0167-7055
Identifier
10.1111/cgf.14818
Publisher
Wiley
Citation
ZHANG, Songheng; MA, Dong; and WANG, Yong.
Don’t peek at my chart: Privacy-preserving visualization for mobile devices. (2023). Computer Graphics Forum. 42, (3), 137-148.
Available at: https://ink.library.smu.edu.sg/sis_research/8107
Additional URL
https://doi.org/10.1111/cgf.14818