Anonymizing Driver and Motor Vehicle Records for Secure Analysis

The collection and analysis of driver and motor vehicle records are vital for transportation research, policy-making, and safety improvements. However, these records often contain sensitive personal information, such as names, addresses, and vehicle identification numbers, which raises significant privacy concerns. This paper explores techniques for anonymizing driver and motor vehicle data to protect privacy while supporting secure and effective analysis. The primary objective is to develop and implement a robust data anonymization methodology that fully anonymizes sensitive information while preserving data utility for analytical purposes. Simulated datasets serve as a testing environment for refining various anonymization techniques, including data masking, pseudonymization, and data swapping. Each method is applied to maximize data protection while maintaining analytical value. The findings demonstrate that sensitive data can be anonymized to meet privacy standards without sacrificing meaningful analysis. Additionally, it highlights the need to balance privacy with data accessibility for researchers and policymakers, along with addressing potential challenges and best practices in the field.