Previous data breaches that occurred in the mobility sector, such as Uber's data leakage in 2016, lead to privacy concerns over confidentiality and the potential abuse of customer data. To protect customer privacy, location-based service (LBS) providers may have the motivation to adopt privacy preservation mechanisms, such as obfuscating data from vehicles or mobile through a trusted data server. However, the efforts for protecting privacy might be in conflict with those for detecting malicious behaviors or misbehaviors by drivers. The reason is that the accuracy of data about vehicle locations and trajectory is crucial in determining whether a vehicle trip is fabricated by adversaries, especially when machine learning methods are adopted by LBS for this purpose. This paper tackles this dilemma situation by evaluating the tradeoff between location privacy and security. Specifically, vehicle trips are obfuscated with 2D Laplace noise that meets the requirement of differential privacy. The obfuscated vehicle trips are then fed into a benchmark Recurrent Neural Network (RNN) that is widely used for detecting anomalous trips. This allows us to investigate the influence of the privacy-preservation technique on model performance. The experiment results suggest that applying Laplace mechanism to achieve high-level of differential privacy in the context of location-based vehicle trips will result in low true-positive or high false-negative rate by the RNN, which is reflected in the area under the curve scores (less than 0.7), which diminishes the value of RNN as more anomalous trips will be classified as normal ones.

中文翻译：

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量化网络安全和位置隐私之间的权衡

之前在移动领域发生的数据泄露事件，例如2016年Uber的数据泄漏，导致对机密性的隐私担忧以及潜在的客户数据滥用问题。为了保护客户隐私，基于位置的服务（LBS）提供商可能会采用隐私保护机制，例如通过可信数据服务器混淆来自车辆或移动设备的数据。但是，保护隐私的努力可能与检测驾驶员的恶意行为或不当行为的努力相冲突。原因是有关车辆位置和轨迹的数据的准确性对于确定对手是否制造了车辆行程至关重要，尤其是当LBS为此目的采用机器学习方法时。本文通过评估位置隐私和安全性之间的权衡来解决这一难题。具体来说，车辆旅行会被2D拉普拉斯（Laplace）噪声所混淆，该噪声满足差异性隐私的要求。然后将经过混淆的车辆行程输入到基准递归神经网络（RNN）中，该网络被广泛用于检测异常行程。这使我们能够研究隐私保护技术对模型性能的影响。实验结果表明，在基于位置的车辆出行的情况下，应用拉普拉斯机制来实现高水平的差异性隐私，将导致RNN的真假率低或假阴性率高，这反映在RNN下的区域中。曲线分数（小于0.7），