提出首个可保护隐私的眼动数据质量验证方法
QualitEye: Public and Privacy-preserving Gaze Data Quality Verification
- 用语义编码眼图信息，只保留验证所需内容
- 在两个数据集上验证准确率高，隐私版仅轻微增加耗时
- 适合需要数据协作又怕泄露隐私的研究者
眼动应用日益依赖大规模数据集，但规模化采集中确保数据质量面临挑战，且多方协作易引发隐私问题。本文提出 QualitEye——首个基于图像的眼动数据质量验证方法。该方法采用新型眼图语义表示，仅保留验证所需信息，剔除无关内容以提升域适应能力。支持公开协作与隐私保护两种场景：前者允许自由交换数据，后者通过改进的私有集合交集协议，确保各方无法泄露原始数据或推导他人眼动特征/标签。在 MPIIFaceGaze 与 GazeCapture 数据集上的评估显示，该方法验证性能优异，隐私版本运行开销极小。QualitEye 为机器学习、人机交互与密码学交叉领域的新方法开辟了道路。
原文摘要 · Abstract (English)
Gaze-based applications are increasingly advancing with the availability of large datasets but ensuring data quality presents a substantial challenge when collecting data at scale. It further requires different parties to collaborate, therefore, privacy concerns arise. We propose QualitEye--the first method for verifying image-based gaze data quality. QualitEye employs a new semantic representation of eye images that contains the information required for verification while excluding irrelevant information for better domain adaptation. QualitEye covers a public setting where parties can freely exchange data and a privacy-preserving setting where parties cannot reveal their raw data nor derive gaze features/labels of others with adapted private set intersection protocols. We evaluate QualitEye on the MPIIFaceGaze and GazeCapture datasets and achieve a high verification performance (with a small overhead in runtime for privacy-preserving versions). Hence, QualitEye paves the way for new gaze analysis methods at the intersection of machine learning, human-computer interaction, and cryptography.
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