The automatic dependent surveillance-broadcast (ADS-B) collects and analyzes massive cognitive aircraft location data, and is essential for safe air traffic controls in the modern aviation industry. In this paper, we present an accurate and efficient aircraft location verification scheme (AEALV) that preserves aircraft location privacy by utilizing grid-based ${k}$ -nearest neighbor ( ${k}$ NN) algorithms. Specifically, we introduce a new approach to efficiently find the ${k}$ nearest grid squares in the ciphertext domain by leveraging vector homomorphic encryption. Further, we present a quick identification technique for aircraft legitimacy by validating claimed locations instead of estimating real locations of the aircraft. This validation only involves calculating encrypted Euclidean distances in a small training circle, thereby significantly saving the verification time of claimed locations. We conduct extensive experiments and evaluate AEALV using real-world data. The results show that AEALV achieves accurate and efficient verification for aircraft locations while maintaining the confidentiality of both aircraft locations and grid data.

中文翻译：

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AEALV：针对 ADS-B 进行准确高效的飞机位置验证

自动相关监视广播 (ADS-B) 收集和分析大量认知飞机位置数据，对于现代航空业的安全空中交通管制至关重要。在本文中，我们提出了一种准确有效的飞机位置验证方案 (AEALV)，该方案通过利用基于网格的方式来保护飞机位置隐私。 ${k}$ -最近的邻居 （ ${k}$ NN）算法。具体来说，我们引入了一种新方法来有效地找到 ${k}$ 通过利用向量同态加密在密文域中最近的方格。此外，我们通过验证声称的位置而不是估计飞机的真实位置，提出了一种快速识别飞机合法性的技术。这种验证只涉及在一个小的训练圈中计算加密的欧几里得距离，从而大大节省了对声称位置的验证时间。我们进行了广泛的实验并使用真实世界的数据评估 AEALV。结果表明，AEALV 实现了对飞机位置的准确有效验证，同时保持了飞机位置和网格数据的机密性。