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Face Privacy Protection Method for Autonomous Sensors Based on Hierarchical Format-Preserving Encryption.

Haojie Ji^1,2, Long Jin¹, Junjie Zhang³

¹Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, China.

Sensors (Basel, Switzerland)

|December 11, 2025

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This summary is machine-generated.

A new hierarchical format-preserving encryption (H-FPE) method protects facial data in connected automated vehicles (CAVs). This advanced technique balances strong privacy with the usability of sensor data for essential tasks.

Area of Science:

Computer Science
Cybersecurity
Automotive Engineering

Background:

Connected automated vehicles (CAVs) utilize advanced sensors that collect sensitive facial biometric data, creating significant privacy risks.
Current automotive data privacy solutions often prioritize security over data usability, hindering the effectiveness of downstream applications.
The need for robust privacy-preserving methods that maintain data utility for environmental perception in CAVs is critical.

Purpose of the Study:

To introduce a novel hierarchical format-preserving encryption (H-FPE) method for facial data privacy in autonomous sensors.
To develop a privacy-preserving framework for face detection that balances encryption strength with functional usability.
To evaluate the effectiveness of the H-FPE method in protecting facial data while preserving essential detection capabilities.

Keywords:

data security face privacy format-preserving encryption functional usability pedestrian detection

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Main Methods:

The study proposes a region-specific encryption strategy within a YOLOv11-based face detection framework.
An SM4-based Feistel structure with pseudo-random functions is employed to ensure RGB value constraints and image format integrity.
Encryption strength is dynamically adjusted based on the importance of different facial regions.

Main Results:

The H-FPE method achieved over 98% entropy efficiency and a 9.4% improvement in entropy increase compared to traditional methods.
Pedestrian detection performance was maintained, with F1-scores exceeding 97% and only a 0.5% difference compared to the thumbnail-preserving encryption (TPE) method.
The proposed method demonstrated substantially stronger privacy protection while preserving critical detection capabilities.

Conclusions:

The hierarchical format-preserving encryption (H-FPE) method effectively balances privacy protection and functional usability for facial data in autonomous sensors.
This approach offers a robust solution for protecting sensitive facial data in CAVs without compromising essential detection tasks.
The region-specific encryption strategy ensures optimal privacy tailored to data sensitivity and application requirements.