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.

翻译：随着眼动追踪在VR和移动平台上的广泛应用，注视数据量持续增长。尽管扫描路径比较对注视行为分析至关重要，但现有方法缺乏面向实际应用的隐私保护能力。我们提出一种基于乱码电路的方法，在半诚实模型下实现安全存储与隐私保护扫描路径比较。该方法支持两种配置：(1)两方设置，数据所有者和处理器在不泄露各自输入的情况下共同计算相似度分数；(2)服务器辅助设置，加密后的扫描路径被存储和处理，同时数据所有者保持离线状态。所有解密与比较操作均在GC内部执行。基于三个眼动追踪数据集的实验评估了保真度、运行时和通信开销，证明MultiMatch、ScanMatch和SubMatch的加密结果与明文结果高度吻合，且运行时和通信开销可控。不同网络条件下的测试表明，该设计在实际隐私保护扫描路径分析中具备可行性，并可扩展至其他基于GC的行为算法。