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

Advanced sensors in connected automated vehicles (CAVs) increasingly collect facial biometric information for environmental perception, posing serious privacy leakage risks. However, existing privacy protection methods for automotive data primarily focus on strict security mechanisms and fail to fully balance data usability. This paper presents a hierarchical format-preserving encryption (H-FPE) method for face privacy protection in autonomous sensors. The proposed method constructs a privacy-preserving framework for face detection based on YOLOv11 by employing a region-specific encryption strategy where the encryption strength is tailored to the importance of different facial regions. The encryption algorithm employs SM4-based Feistel structures with pseudo-random functions to ensure RGB value constraints while maintaining image format integrity. Experimental evaluation results in diverse scenarios demonstrate that the proposed privacy encryption method achieves superior privacy protection performance. In terms of encryption strength, the method achieves entropy efficiency exceeding 98%, with an average entropy increase of 0.77 bits, representing an improvement of approximately 9.4% over the traditional thumbnail-preserving encryption (TPE) method. Considering the usability of downstream tasks, the proposed method preserves pedestrian detection performance, with F1-scores exceeding 97% in selected scenarios, demonstrating a 0.5% difference compared to TPE while providing substantially stronger privacy protection. The H-FPE method effectively balances privacy protection and functional usability, offering a robust solution for facial data protection in autonomous sensor applications while preserving essential detection capabilities.