As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve differential privacy and empirical data manipulations, previous research has not focused on methods for scanpaths. We introduce a novel privacy-preserving scanpath comparison protocol designed for the widely used Needleman-Wunsch algorithm, a generalized version of the edit distance algorithm. Particularly, by incorporating the Paillier homomorphic encryption scheme, our protocol ensures that no private information is revealed. Furthermore, we introduce a random processing strategy and a multi-layered masking method to obfuscate the values while preserving the original order of encrypted editing operation costs. This minimizes communication overhead, requiring a single communication round for each iteration of the Needleman-Wunsch process. We demonstrate the efficiency and applicability of our protocol on three publicly available datasets with comprehensive computational performance analyses and make our source code publicly accessible.

中文翻译：

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普遍眼动追踪的隐私保护扫描路径比较

随着眼动追踪在基于屏幕的设备和头戴式显示器中变得普遍，有关眼动追踪数据的隐私问题也随之升级。虽然最先进的隐私保护眼动追踪方法主要涉及差分隐私和经验数据操作，但之前的研究并未关注扫描路径方法。我们引入了一种新颖的隐私保护扫描路径比较协议，专为广泛使用的 Needleman-Wunsch 算法而设计，该算法是编辑距离算法的通用版本。特别是，通过结合 Paillier 同态加密方案，我们的协议确保不会泄露私人信息。此外，我们引入了随机处理策略和多层掩码方法来混淆值，同时保留加密编辑操作成本的原始顺序。这最大限度地减少了通信开销，Needleman-Wunsch 过程的每次迭代都需要单轮通信。我们通过全面的计算性能分析在三个公开可用的数据集上证明了我们的协议的效率和适用性，并使我们的源代码可供公开访问。