Generating Realistic Passenger Name Records with Privacy Compliance for Security Analysis

Abstract

Passenger Name Record (PNR) data is essential for transportation analysis and security research, particularly in surveillance and threat detection. However, stringent security and privacy concerns limit access to real PNR data. This study presents a methodology for generating synthetic PNR data that not only replicates statistical properties but also reconstructs passenger social networks, models travel behaviours and preserves individual travel histories for security and mobility analysis. Our approach generates detailed, individual-level data—including passengers, bookings, and flights—while maintaining spatial, temporal, and chronological consistency to ensure realistic movement patterns while upholding privacy. This methodology offers a privacy-preserving alternative for transportation security and behavioural research, expanding access to high-quality data for future studies.