Quantifying the Tradeoff Between Cybersecurity and Location Privacy

Previous data breaches occurred in the mobility sector, such as Uber's data leakage in 2016, lead to privacy concerns over the 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 presenting a quantitative framework for evaluating the tradeoff between location privacy and security. Specifically, we assume a trust data server will obfuscate vehicle trips by adding 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 the performance of the Machine learning model. The results of our experiments suggest that applying Laplace mechanism to achieve high-level of differential privacy in the context of location-based vehicle trips will introduce high false negative rate by the RNN, which diminishes the value of RNN as more malicious trips will be classified as normal ones.