RDP：多尺度稀疏化子空间中用于面部特征保护的排序差分隐私
RDP: Ranked Differential Privacy for Facial Feature Protection in Multiscale Sparsified Subspace
AI总结：
本文提出排序差分隐私（RDP）方法，在多尺度稀疏化子空间中按特征系数权重分配拉普拉斯噪声以保护面部特征，并通过NA解析法和LMGD数值法求解最优噪声参数，实验表明其在隐私预算0.2时PSNR比现有方法高约10 dB。
摘要：
随着个人面部图像在应用公共数据库中的广泛共享，人脸识别系统面临着被潜在对手攻破的现实威胁，这些对手能够获取用户的面部图像并利用它们入侵人脸识别系统。在本文中，我们提出了一种在多尺度稀疏化特征子空间中的新型隐私保护方法，通过考虑特征系数的影响或权重排序对隐私预算的作用来保护敏感面部特征，该方法被命名为“排序差分隐私（Ranked Differential Privacy, RDP）”。在多尺度特征分解之后，根据几何叠加方法，将轻量级的拉普拉斯噪声添加到降维后的稀疏化特征系数上。然后，我们严格证明了RDP满足差分隐私。之后，针对在给定面部特征隐私预算下最大化带消毒噪声的受保护人脸图像可视化质量效用的约束优化问题，建立了非线性拉格朗日乘子（Lagrange Multiplier, LM）方法。接着，提出了两种方法来求解非线性LM问题并获得最优噪声尺度参数：1）解析归一化近似（Normalization Approximation, NA）方法，采用相同的平均噪声尺度参数，适用于实时在线应用；2）LM优化梯度下降（LMGD）数值方法，通过迭代更新获得非线性解，适用于更精确的离线应用。在两个真实世界数据集上的实验结果表明，我们提出的RDP优于其他最先进的方法：在隐私预算为0.2时，RDP的PSNR（峰值信噪比）比所有对比方法中最高的PSNR高出约10 dB（即为其10倍）。
英文摘要：
With the widespread sharing of personal face images in applications' public databases, face recognition systems faces real threat of being breached by potential adversaries who are able to access users' face images and use them to intrude the face recognition systems. In this paper, we propose a novel privacy protection method in the multiscale sparsified feature subspaces to protect sensitive facial features, by taking care of the influence or weight ranked feature coefficients on the privacy budget, named "Ranked Differential Privacy (RDP)". After the multiscale feature decomposition, the lightweight Laplacian noise is added to the dimension-reduced sparsified feature coefficients according to the geometric superposition method. Then, we rigorously prove that the RDP satisfies Differential Privacy. After that, the nonlinear Lagrange Multiplier (LM) method is formulated for the constraint optimization problem of maximizing the utility of the visualization quality protected face images with sanitizing noise, under a given facial features privacy budget. Then, two methods are proposed to solve the nonlinear LM problem and obtain the optimal noise scale parameters: 1) the analytical Normalization Approximation (NA) method with identical average noise scale parameter for real-time online applications; and 2) the LM optimization Gradient Descent (LMGD) numerical method to obtain the nonlinear solution through iterative updating for more accurate offline applications. Experimental results on two real-world datasets show that our proposed RDP outperforms other state-of-the-art methods: at a privacy budget of 0.2, the PSNR (Peak Signal-to-Noise Ratio) of the RDP is about ~10 dB higher than (10 times as high as) the highest PSNR of all compared methods.
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