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.

翻译：随着移动数据可视化的普及，其隐私风险日益受到关注，尤其是肩窥攻击。受先前视觉错觉研究的启发，我们提出BAIT，一种通过将诱饵可视化叠加在给定可视化之上来自动生成隐私保护可视化的新方法。该方法通过调整诱饵可视化的不同视觉通道（如形状、位置、倾斜度、大小、颜色和空间频率），使邻近的可视化所有者能够清晰辨识原始可视化，同时让远处的肩窥者被诱饵可视化误导。我们显式建模了不同观看距离下的人类感知效应，以优化诱饵可视化的设计。隐私保护示例和两项深入的用户研究表明，BAIT在受控实验室研究和实际场景中均具有有效性。