Abstract
This paper aims to propose a novel machine learning (ML) approach incorporating Homomorphic Encryption (HE) to address privacy limitations in Unmanned Aerial Vehicles (UAV)-based face detection. Due to challenges related to distance, altitude, and face orientation, high-resolution imagery and sophisticated neural networks enable accurate face recognition in dynamic environments. However, privacy concerns arise from the extensive surveillance capabilities of UAVs. To resolve this issue, we propose a novel framework that integrates HE with advanced neural networks to secure facial data throughout the inference phase. This method ensures that facial data remains secure with minimal impact on detection accuracy. Specifically, the proposed system leverages the Cheon-Kim-Kim-Song (CKKS) scheme to perform computations directly on encrypted data, optimizing computational efficiency and security. Furthermore, we develop an effective data encoding method specifically designed to preprocess the raw facial data into CKKS form in a Single-Instruction-Multiple-Data (SIMD) manner. Building on this, we design a secure inference algorithm to compute on ciphertext without needing decryption. This approach not only protects data privacy during the processing of facial data but also enhances the efficiency of UAV-based face detection systems. Experimental results demonstrate that our method effectively balances privacy protection and detection performance, making it a viable solution for UAV-based secure face detection. Significantly, our approach (while maintaining data confidentially with HE encryption) can still achieve an accuracy of less than 1% compared to the benchmark without using encryption.
Abstract (translated)
本文旨在提出一种结合同态加密（HE）的新型机器学习（ML）方法，以解决无人机（UAV）面部检测中的隐私限制问题。由于距离、高度和面部方向的变化带来的挑战，高分辨率图像和复杂的神经网络能够在动态环境中实现准确的人脸识别。然而，UAV的强大监控能力引发了隐私担忧。为了解决这一问题，我们提出了一种新颖的框架，该框架将HE与高级神经网络相结合，在推理阶段保护面部数据的安全性。这种方法确保了面部数据在安全的前提下不会显著影响检测准确性。 具体而言，所提出的系统利用Cheon-Kim-Kim-Song（CKKS）方案直接对加密数据进行计算，从而优化了计算效率和安全性。此外，我们开发了一种有效的数据编码方法，该方法专门用于将原始面部数据预处理为适合CKKS的格式，并采用单指令多数据流（SIMD）方式进行处理。在此基础上，设计了一个安全推理算法，在不解密的情况下对加密后的数据进行计算。这种方法不仅在处理人脸数据时保护了数据隐私，还提高了基于UAV的人脸检测系统的效率。 实验结果表明，我们提出的方法能够有效平衡隐私保护与检测性能之间的关系，使其成为基于UAV的面部识别系统的一种可行解决方案。特别值得注意的是，在保持数据机密性的同时（通过HE加密），我们的方法相比不使用加密的标准基准仍能达到不到1%误差率的准确性。
URL
https://arxiv.org/abs/2507.09860
