论文标题
隐藏和寻求 - 在无人驾驶飞机的远程标识中保存位置隐私和实用性
Hide and Seek -- Preserving Location Privacy and Utility in the Remote Identification of Unmanned Aerial Vehicles
论文作者
论文摘要
由于商业无人机频繁未经授权的访问权限到关键基础设施（CIS），例如机场和炼油厂，因此美国联邦航空电子管理局（FAA）最近发布了一项新的规范，即远程偏远。上述规则要求所有无人驾驶汽车（UAV）必须无线广播有关其身份和位置的信息，以允许立即入侵归属。但是，执行此类规则对无人机操作员提出了严重的关注，尤其是在位置隐私和跟踪威胁方面，仅举几例。实际上，通过简单地窃听无线通道，对手可以知道无人机的确切位置并跟踪它，并在无人机的路径源和目的地上获取敏感信息。在本文中，我们调查了位置隐私与数据实用程序之间的权衡，当通过差异隐私技术掩盖广播的位置时，可以提供给无人机。利用已经在基于位置的服务（LBS）的背景下采用的地理可区分性（地理印度）的概念，我们表明可以增强无人机的隐私，而不会阻止CI操作员及时检测未经授权的入侵。特别是，我们的实验表明，当无人机的位置以1.959 km的平均距离混淆时，精心设计的无人机检测系统可以检测到97.9％的入侵，平均检测延迟为303.97毫秒。无人机必须通过不可忽略的误报概率来权衡这种增强的位置隐私，即被发现是入侵的，而不是真正入侵的无灯区。无人机和CI操作员可以在FAA的帮助下解决这种模棱两可的情况，这是后者是唯一可以揭示无人机实际位置的情况。
Due to the frequent unauthorized access by commercial drones to Critical Infrastructures (CIs) such as airports and oil refineries, the US-based Federal Avionics Administration (FAA) recently published a new specification, namely RemoteID. The aforementioned rule mandates that all Unmanned Aerial Vehicles (UAVs) have to broadcast information about their identity and location wirelessly to allow for immediate invasion attribution. However, the enforcement of such a rule poses severe concerns on UAV operators, especially in terms of location privacy and tracking threats, to name a few. Indeed, by simply eavesdropping on the wireless channel, an adversary could know the precise location of the UAV and track it, as well as obtaining sensitive information on path source and destination of the UAV. In this paper, we investigate the trade-off between location privacy and data utility that can be provided to UAVs when obfuscating the broadcasted location through differential privacy techniques. Leveraging the concept of Geo-Indistinguishability (Geo-Ind), already adopted in the context of Location-Based Services (LBS), we show that it is possible to enhance the privacy of the UAVs without preventing CI operators to timely detect unauthorized invasions. In particular, our experiments showed that when the location of an UAV is obfuscated with an average distance of 1.959 km, a carefully designed UAV detection system can detect 97.9% of invasions, with an average detection delay of 303.97 msec. The UAVs have to trade-off such enhanced location privacy with a non-negligible probability of false positives, i.e., being detected as invading while not really invading the no-fly zone. UAVs and CI operators can solve such ambiguous situations later on through the help of the FAA, being this latter the only one that can unveil the actual location of the UAV.
