标题： 基于同态加密和边缘计算的安全高效无人机人脸检测 显示英文标题

标题： Secure and Efficient UAV-Based Face Detection via Homomorphic Encryption and Edge Computing

摘要： 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，以解决基于无人机（UAV）的人脸检测中的隐私限制问题。 由于距离、高度和人脸方向等相关挑战，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，无人机的广泛监控能力引发了隐私问题。 为了解决这个问题，我们提出了一种新颖的框架，将HE与先进的神经网络相结合，以在推理阶段保护面部数据。 该方法确保面部数据在检测准确性影响最小的情况下保持安全。 具体而言，所提出的系统利用Cheon-Kim-Kim-Song（CKKS）方案对加密数据直接进行计算，优化计算效率和安全性。 此外，我们开发了一种有效的数据编码方法，专门设计用于以单指令多数据（SIMD）方式将原始面部数据预处理为CKKS形式。 在此基础上，我们设计了一个安全的推理算法，在不需要解密的情况下对密文进行计算。 这种方法不仅在处理面部数据时保护了数据隐私，还提高了基于无人机的人脸检测系统的效率。 实验结果表明，我们的方法有效平衡了隐私保护和检测性能，使其成为基于无人机的安全人脸检测的可行解决方案。 显著的是，我们的方法（在使用HE加密保持数据机密性的同时）仍能实现与不使用加密的基准相比小于1%的准确率。 显示英文摘要

摘要： 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.