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arXiv · 2601.18497

BAIT: Visual-illusion-inspired Privacy Preservation for Mobile Data Visualization

Sizhe Cheng·Songheng Zhang·Dong Ma·Yong Wang
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Abstract

With the prevalence of mobile data visualizations, there have been growing concerns about their privacy risks, especially shoulder surfing attacks. Inspired by prior research on visual illusion, we propose BAIT, a novel approach to automatically generate privacy-preserving visualizations by stacking a decoy visualization over a given visualization. It allows visualization owners at proximity to clearly discern the original visualization and makes shoulder surfers at a distance be misled by the decoy visualization, by adjusting different visual channels of a decoy visualization (e.g., shape, position, tilt, size, color and spatial frequency). We explicitly model human perception effect at different viewing distances to optimize the decoy visualization design. Privacy-preserving examples and two in-depth user studies demonstrate the effectiveness of BAIT in both controlled lab study and real-world scenarios.

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Sizhe Cheng, Songheng Zhang, Dong Ma, Yong Wang. 2026-01-26. BAIT: Visual-illusion-inspired Privacy Preservation for Mobile Data Visualization. https://arxiv.org/abs/2601.18497

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