QualitEye: Public and Privacy-preserving Gaze Data Quality Verification

2025-06-06

AI Summary

Addressing the dual challenges of quality assurance and cross-institutional privacy preservation in large-scale eye-tracking data collection, this paper proposes the first image-based verifiable framework for eye-tracking data quality. Methodologically: (1) it introduces a semantic eye-chart representation enabling cross-domain adaptability; (2) it establishes a hybrid public–private verification mechanism integrating secure multi-party computation (MPC) and private set intersection (PSI) protocols, enabling collaborative quality validation without sharing raw data. Evaluated on MPIIFaceGaze and GazeCapture, the framework achieves high-accuracy quality classification, with the privacy-preserving variant incurring less than 3% runtime overhead. This work constitutes the first solution that unifies verifiability and privacy protection for eye-tracking data quality, establishing a trustworthy infrastructure for multi-center eye-tracking research.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• Ensuring gaze data quality in large-scale collection
• Addressing privacy concerns in collaborative gaze data verification
• Developing a method for image-based gaze quality verification

Innovation

• Semantic eye image representation for verification
• Public and privacy-preserving data exchange
• Private set intersection protocols adaptation