Secure Storage and Privacy-Preserving Scanpath Comparison via Garbled Circuits in Eye Tracking

TL;DR

This paper introduces a garbled-circuit based method for secure, privacy-preserving scanpath comparison in eye tracking, enabling analysis without revealing sensitive gaze data.

Contribution

It presents a novel GC-based framework supporting two configurations for privacy-preserving scanpath comparison in eye tracking, with practical evaluation on real datasets.

Findings

Secure comparison results closely match plaintext outcomes.

The approach demonstrates manageable runtime and communication overhead.

The method is feasible for real-world privacy-preserving gaze analysis.

Abstract

With the growing use of eye tracking on VR and mobile platforms, gaze data is increasing. While scanpath comparison is important to gaze behavior analysis, existing methods lack privacy-preserving capabilities for real-world use. We present a garbled-circuit (GC)-based approach enabling secure storage and privacy-preserving scanpath comparison under the semi-honest model. It supports two configurations: (1) a two-party setting where the data owner and processor jointly compute similarity scores without revealing their inputs, and (2) a server-assisted setting where encrypted scanpaths are stored and processed while the data owner remains offline. All decryption and comparison operations are executed inside the GC. Experiments on three eye-tracking datasets evaluate fidelity, runtime, and communication, and show secure results for MultiMatch, ScanMatch, and SubsMatch closely match plaintext outcomes, with manageable runtime and communication overhead. Tests under various network conditions indicate that the design remains feasible for real-world privacy-preserving scanpath analysis and can be extended to other GC-based behavioral algorithms.