RDP: Ranked Differential Privacy for Facial Feature Protection in Multiscale Sparsified Subspace

TL;DR

This paper introduces Ranked Differential Privacy (RDP), a novel method for protecting facial features in multiscale sparsified subspaces, ensuring privacy while maintaining high image utility, validated through experiments showing superior performance.

Contribution

The paper proposes RDP, a new privacy protection approach that incorporates feature ranking and noise addition, with optimization techniques for enhanced face image privacy and utility.

Findings

RDP achieves about 10 dB higher PSNR at privacy budget 0.2.

RDP outperforms state-of-the-art methods in facial feature privacy.

The method satisfies rigorous differential privacy guarantees.

Abstract

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.