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.

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