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Accelerator for Trajectory Anonymization Using Map Matching
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AI
DOI：10.1109/ACCESS.2024.3479948.png)
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Abstract
En 中文
This study is focused on hardware-based anonymization of location data for addressing privacy concerns while maintaining data utility in applications such as traffic-congestion prediction. The anonymization process involves approximating user location data to map intersections, identifying trajectories, and applying k-anonymization. However, the processing time in software implementation should be minimized. For real-time services, the process must be completed within 30 s to 1 min, which corresponds to the information update interval. Service requirements cannot be met when the volume of data and the scale of maps increase. To address this issue, we implemented a hardware accelerator on an field programmable gate array to perform the process with the aim of reducing the processing time. As a result, the maximum throughput of the proposed mechanism was approximately 150,000 data/s, which surpasses the target value of 130,000 data/s. The data retention rate, which represents information loss, was approximately 90% for the commonly used range of $10\le k\le 20$ . The difference in the data retention rate compared with the software implementation was approximately 1%. These results confirmed the effectiveness of the anonymization results obtained using the proposed mechanism.
Keywords:
Trajectory
Data privacy
Information integrity
Information filtering
Field programmable gate arrays
Real-time systems
Edge computing
Memory management
Arrays
Location awareness
FPGA
anonymization
edge computing