Differentially-Private Analytics for Qatar Traffic Data

As artificial intelligence (AI) systems continue to evolve, striking a balance between their growing data requirements and the need to maintain privacy and security has become a critical research focus. In the context of Qatar, AI applications are increasingly utilized in various sectors, such as transportation and healthcare, bringing about significant improvements in efficiency and effectiveness. Ensuring the privacy of sensitive information in these domains is important as the utilization of data plays a significant role in driving these innovations.

This thesis investigates a technique for releasing real geo-tagged traffic data from Qatar with Differential Privacy (DP), aiming to protect sensitive information while supporting AI analysis. The research explores the principles of DP and various DP-compliant mechanisms, techniques for aligning the real-world dataset with the simulated dataset, evaluating the feasibility of various sanitization algorithms for the Qatar dataset, and understanding their trade-offs. Subsequently, these findings demonstrate the effectiveness of applying DP on Qatar traffic data, not only preserving data privacy and quality but also facilitating AI advancements in the region. With a growing emphasis on data security and privacy, the adoption of DP principles and DP-compliant methods will be essential in fostering trust and encouraging the widespread use of AI in various sectors, driving progress and innovation.