History-Aware Trajectory k-Anonymization Using an FPGA-Based Hardware Accelerator for Real-Time Location Services

TL;DR

This paper presents a novel FPGA-based system for real-time, history-aware trajectory k-anonymization that improves data utility and privacy preservation by integrating historical travel behavior into route anonymization.

Contribution

It introduces a new methodology and FPGA architecture that combines historical trajectory data with shortest-path computations for enhanced anonymization.

Findings

Achieves over 6,000 records/sec throughput in real-time processing.

Increases data retention by up to 1.2% over previous shortest-path-only methods.

Better preserves major arterial roads, enhancing behavioral accuracy.

Abstract

Our previous work established the feasibility of FPGA-based real-time trajectory anonymization, a critical task for protecting user privacy in modern location-based services (LBS). However, that pioneering approach relied exclusively on shortest-path computations, which can fail to capture re- alistic travel behavior and thus reduce the utility of the anonymized data. To address this limitation, this paper introduces a novel, history-aware trajectory k-anonymization methodology and presents an advanced FPGA-based hardware architecture to implement it. Our proposed architecture uniquely integrates par- allel history-based trajectory searches with conventional shortest- path finding, using a custom fixed-point counting module to ac- curately weigh contributions from historical data. This approach enables the system to prioritize behaviorally common routes over geometrically shorter but less-traveled paths. The FPGA implementation demonstrates that our new architecture achieves a real-time throughput of over 6,000 records/s, improves data retention by up to 1.2% compared to our previous shortest-path- only design, and preserves major arterial roads more effectively. These results signify a key advancement, enabling high-fidelity, history-aware anonymization that preserves both privacy and behavioral accuracy under the strict latency constraints of LBS.