Privacy-Preserving Computation in Trustworthy Face Recognition: A Comprehensive Survey
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摘要: 论文聚焦人脸识别生态，系统梳理了面向人脸识别可信应用的隐私保护计算研究进展。首先，概述了人脸识别系统的基本架构与流程，剖析非授权采集、信息泄露、梯度泄露、成员推理、人脸重建及非授权识别等关键隐私风险。随后，围绕数据变换、分布式、图像合成和对抗扰动四类主流隐私保护范式，解析加密计算、联邦学习、频域学习、特征模板保护、合成图像训练、身份保持匿名化、虚拟身份识别、差分隐私、重建攻击防御与对抗性隐私保护等十类代表性技术。最后，展望未来研究方向，包括隐私保护计算的效率提升、生成式大模型带来的新机遇与挑战、新型识别范式的构建以及标准化评估体系的建立。论文旨在为可信人脸识别研究提供系统性参考，推动其在信息物理系统中的安全与可信应用，进一步强化个人信息保护。Abstract:
Significance With the widespread deployment of face recognition in Cyber-Physical Systems (CPS), including smart cities, intelligent transportation, and public safety infrastructures, privacy leakage has become a central concern for both academia and industry. Unlike many biometric modalities, face recognition operates in highly visible and loosely controlled environments such as public spaces, consumer devices, and online platforms, where facial image acquisition is effortless and pervasive. This exposure makes facial data especially vulnerable to unauthorized collection and misuse. Insufficient protection may lead to identity theft, unauthorized tracking, and deepfake generation, undermining individual rights and eroding trust in digital systems. Consequently, facial data protection is not merely a technical problem but a critical societal and ethical challenge. This work is significant in that it integrates fragmented research efforts across computer vision, cryptography, and privacy-preserving computation, providing a unified perspective to guide the development of trustworthy face recognition ecosystems that balance usability, compliance, and public trust. Contributions This paper systematically reviews recent advances in privacy-preserving computation for face recognition, covering both theoretical foundations and practical implementations. It begins by examining the core architecture and application pipeline of face recognition systems, identifying privacy risks at each stage. At the data collection stage, unauthorized or covert capture of facial images introduces immediate risks of misuse. During model training and deployment, gradient leakage, membership inference, and overfitting can expose sensitive information about individuals included in training data. At the inference stage, adversaries may reconstruct facial images, perform unauthorized recognition, or link identities across datasets, compromising anonymity.To address these threats, the paper categorizes existing approaches into four major privacy-preserving paradigms: data transformation, distributed collaboration, image generation, and adversarial perturbation. Within these categories, ten representative techniques are analyzed. Cryptographic computation, including homomorphic encryption and secure multiparty computation, enables recognition without revealing raw data but often incurs high computational overhead. Frequency-domain learning transforms images into spectral representations to suppress identifiable details while retaining discriminative features. Federated learning decentralizes training to reduce centralized data exposure, though it remains vulnerable to gradient inversion attacks. Image generation techniques, such as face synthesis and virtual identity modeling, reduce reliance on real facial data for training and testing. Differential privacy introduces calibrated noise to provide statistical privacy guarantees, while face anonymization obscures identifiable traits to protect visual privacy. Template protection and anti-reconstruction mechanisms defend stored features against reverse engineering, and adversarial privacy protection introduces imperceptible perturbations that disrupt machine recognition while preserving human perception.In addition, several representative studies from each category are examined in depth. The commonly used evaluation datasets are summarized, and a comparative analysis is conducted across multiple dimensions, including face recognition performance, privacy protection effectiveness, and practical usability, thereby systematically outlining the strengths and limitations of different types of methods. Prospects Looking forward, several research directions are identified. A primary challenge is achieving a dynamic balance between privacy protection and system utility, as excessive protection can degrade recognition performance while insufficient safeguards expose users to unacceptable risks. Adaptive mechanisms that adjust privacy levels based on context, task requirements, and user consent are therefore essential. Another promising direction is the development of inherently privacy-aware recognition paradigms, such as representations designed to minimize identity leakage by construction.Equally important is the establishment of standardized evaluation frameworks for privacy risk and usability, enabling reproducible benchmarking and facilitating real-world adoption. The emergence of generative foundation models, including diffusion and large multimodal models, further reshapes the landscape. While such models enable synthetic data generation and controllable identity representations, they also empower more sophisticated attacks such as high-fidelity face reconstruction and impersonation. Addressing these dual effects will require interdisciplinary collaboration spanning computer vision, cryptography, law, and ethics, alongside regulatory support and continuous methodological innovation. Conclusions This paper provides a comprehensive reference for researchers and practitioners working on trustworthy face recognition. By integrating advances across multiple disciplines, it aims to promote the development of effective facial privacy protection technologies and support the secure, reliable, and ethically responsible deployment of face recognition in real-world scenarios. Ultimately, the goal is to establish face recognition as a trustworthy component of Cyber-Physical Systems, balancing functionality, privacy, and societal trust. -
图 8 基于虚拟身份的隐私保护人脸识别示意图。该类方法为每个真实身份生成一个外观不同的虚拟人脸用于系统注册与识别。虚拟身份在视觉和特征层面均与原始身份分离，从而实现图像层与特征层的双重隐私保护。
表 1 代表性研究工作的实验方法与性能对比
类别 应对
风险代表性研究 测试数据集 人脸识别性能(%) 隐私保护性能 可用性评估 加密
计算(r4)
(r5)Secure Face-FH[18] LFW, IJB-A, IJB-B,
CASIA-WebFaceTAR↑ (67.89-99.11) 理论性隐私保证 匹配耗时(2.45 ms)
存储开销(16 KB)Efficient-PPFR[37] LFW TAR↑ (97.72) 注册耗时(2.614 s)
匹配耗时(2.718 s)Gao[38] Yale, ORL ACC↑ (89.29-96.15) 识别总耗时(2.467 s) 频域学习 (r2) PPFR-FD[9] LFW, CFP-FP, AgeDB, CPLFW, CALFW, VGGFace2 ACC↑ (90.78-99.68) SSIM↓ (0.713), PSNR↓ (15.66) N/A DuetFace[10] LFW, CFP-FP, AgeDB, CPLFW, CALFW, IJB-B, IJB-C ACC↑ (92.10-99.82) SSIM↓ (0.866), PSNR↓ (19.88) PartialFace[40] ACC↑ (92.03-99.80) SSIM↓ (0.591), PSNR↓ (13.70) 模板保护 (r5) MLP-Hash[21] LFW, MOBIO TAR↑ (90.90-100) 不可链接性↓(0.01)
不可逆性↑(9.05)保护执行时间↓(62 μs) Simhash[49] FEI, CASIA-WebFace,
LFWTAR↑ (94.06-100) 字典攻击复杂度↑(2411-2430次)
不可连接性↓(0.039-0.043)
不可逆性↓(0.04-0.08)注册耗时↓(10.1-10.8 ms)
匹配耗时↓(69-72 μs)SlerpFace[19] LFW, CFP-FP, AgeDB, CALFW, CPLFW, IJB-B, IJB-C ACC↑ (88.90-99.42) 不可链接性↓(0.05)
暴力破解次数↑(3.649次)注册耗时↓(0.35 s)
匹配耗时↓(0.17 s)联邦学习 (r3) FedFace[54] LFW, IJB-A, IJB-C ACC↑ (83.79-99.28) 经验性隐私保证 N/A FedFR[59] IJB-C ACC↑ (85.21) 合成图像训练 (r1)
(r3)HyperFace[73] LFW, CFP-FP, AgeDB, CPLFW, CALFW ACC↑ (87.07-98.67) 经验性隐私保证 N/A CemiFace[5] ACC↑ (88.86-99.22) MorphFace[74] ACC↑ (90.07-99.35) 保持身份匿名化 (r2) PRO-Face[79] LFW, CelebA, VGGFace2 TAR↑ (88.4-94.7) SSIM↓ (0.527-0.875)
LPIPS↑ (0.111-0.638)N/A Li[78] CelebA, VGGFace2 TAR↑ (61-89) 属性识别准确率↓，如
年龄23-49 %，性别13-41 %，
种族10-35 %FID↓ (41.64-54.04) Wang[82] CelebA, VGGFace2 AUC↑ (88.5-96.9) SSIM↓ (0.306-0.315)
LPIPS↑ (0.559-0.588)
MAE↑ (0.251-0.256)HPS↓ (2.315-3.831)
EDR↑ (69.2-73 %)虚拟身份匹配 (r2)
(r5)IVFG[84] LFW, CelebA AUC↑ (99.4-99.9)
EER↓ (1.8-3.5)PSR↑ (98.8 %) FDR↑ (100 %), FID↓ (6.17) CanFG[87] CelebA, VGGFace2 AUC↑ (95.1-98.8), EER↓ (4.5-10.1) PSR↑(98.2-99.2 %) FID↓ (9.43), SSIM↑ (0.823) KFAAR[88] LFW, CelebA AUC↑ (97.3-99.2), EER↓ (8.9-9.2) PSR↑ (92.2-96.2 %) FDR↑ (100 %), EDR↑ (80.5-83.3 %), FID↓ (6.82-7.29) 差分隐私 (r2)
(r3)
(r4)
(r5)Mao[91] LFW ACC↑ (82) 理论性隐私保证 N/A PEEP[22] LFW, CFP-FP, AgeDB, CPLFW, CALFW, IJB-B, IJB-C ACC↑ (5.82-98.41) Ji[39] ACC↑ (89.33-99.48) PSNR↓ (14.28), COS↓ (0.214) 重建攻击防御 (r5) AdvFace[20] LFW, CFP-FP, AgeDB-30 ACC↑ (86.35-97.78) SSIM↓ (0.28), PSNR↓ (6.97),
MSE↑ (0.206), SRRA↓ (4.03%)N/A MinusFace[43] LFW, CFP-FP, AgeDB, CPLFW, CALFW, IJB-B, IJB-C ACC↑ (91.90-99.78) SSIM↓ (0.50), PSNR↓ (10.98) FaceObfuscator[97] ACC↑ (88.48-99.68) SSIM↓ (0.471), PSNR↓ (12.71),
MSE↑ (0.057), COS↓ (0.004)存储开销：98 KB/pic
推理耗时：1.18 ms/pic对抗性隐私保护 (r2)
(r6)OPOM[101] Celeb-1M, LFW N/A PSR (1:N)↑ (78-86.6 %@R-1-U,
69.4-79.3 %@R-5-U)N/A CLIP2Protect[105] CelebA, LFW PSR (1:1)↑ (64.9 %),
PSR (1:N)↑ (82.2 %@R-1-U, 23.4 %@R-1-T)PSNR↑ (19.31),
SSIM↑ (0.75),
FID↓ (26.5)Salar[107] CelebA PSR (1:1)↑ (79.17 %) PSNR↑ (27.72),
SSIM↑ (0.84), FID↓ (26.5)
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