Enhancing Differential Privacy in Mobility Data Release with Deep Reinforcement Learning

In the era of data-driven decision-making and the pervasive use of machine learning techniques,the release of sensitive mobility data raises significant privacy concerns. Traditional methods ofanonymization and aggregation often fall short in protecting individual privacy, especially in theface of increasingly sophisticated attacks. In this paper, we propose a novel approach to enhancethe privacy guarantees of mobility data release using Deep Reinforcement Learning (DRL).Mobility data, which encompasses information about individuals' movements and locations, hasbecome increasingly valuable for various applications, including urban planning, transportationmanagement, and location-based services. However, the widespread collection and disseminationof such data have raised serious privacy concerns, as they can reveal sensitive information aboutindividuals' daily routines, habits, and behaviors.Differential privacy has emerged as a rigorous mathematical framework for addressing privacyconcerns in data release. It provides strong privacy guarantees by ensuring that the presence orabsence of any individual's data in the released dataset does not significantly impact the output ofany analysis. However, achieving differential privacy in the context of mobility data release ischallenging due to the sequential and highly correlated nature of location trajectories.To overcome these challenges, we propose a novel approach that leverages Deep ReinforcementLearning (DRL) techniques. DRL has demonstrated remarkable success in learning complexsequential decision-making tasks and has been applied to various domains, including robotics,gaming, and natural language processing. In our approach, we formulate the problem ofprivacy-preserving mobility data release as a sequential decision-making task, where an agentlearns to generate synthetic trajectories that mimic the underlying mobility patterns whilepreserving privacy.Our method consists of two main stages: policy learning and data perturbation. In the policylearning stage, the DRL agent learns to generate synthetic trajectories by interacting with anenvironment that simulates real-world mobility patterns. The agent receives feedback on theprivacy and utility of its generated trajectories and updates its policy accordingly usingreinforcement learning techniques. In the data perturbation stage, we apply differential privacymechanisms to further enhance privacy guarantees by adding noise to the generated trajectories.We evaluate the performance of our approach on real-world mobility datasets, including taxiGPS traces and mobile phone location data. Our experiments demonstrate that our methodoutperforms existing differential privacy techniques in terms of both privacy guarantees andutility preservation. Specifically, our approach achieves stronger privacy guarantees whilemaintaining comparable utility in various mobility analytics tasks, such as origin-destinationanalysis and trajectory prediction.