Modern transportation systems generate massive amounts of data, including where and how vehicles and people move, traffic conditions, road conditions, and videos captured during actual trips. This includes detailed information about everyday driving behavior collected by cameras and sensors in cars and on roads. These datasets are essential for improving traffic safety, reducing congestion, and supporting the development of advanced technologies such as self-driving cars. However, they often contain sensitive personal details about individuals, making it difficult to share among traffic authorities, companies, and research institutions. This project addresses this challenge by developing secure methods for sharing transportation data while protecting individual privacy, serving the national interest by advancing transportation safety, supporting economic competitiveness in autonomous vehicle technologies, and strengthening infrastructure resilience through improved data-driven decision making. This project develops a comprehensive privacy-preserving platform for sharing diverse intelligent transportation systems data across different entities. The research targets multiple data types, including vehicle and road user information such as speed, travel times, and trajectories, as well as infrastructure data including traffic flow, control states, and videos. The project focuses particularly on naturalistic driving data collected by in-vehicle sensors and mobile devices. The