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

arXiv: https://arxiv.org/abs/2408.00294 · HTML: https://mlsanity.com/paper/2408.00294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models](https://mlsanity.com/paper/2101.05084/) (2020)
- [Privacy Preserving Face Recognition Utilizing Differential Privacy](https://mlsanity.com/paper/2005.10486/) (2020)
- [Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain](https://mlsanity.com/paper/2207.07316/) (2022)
- [Privacy-preserving Adversarial Facial Features](https://mlsanity.com/paper/2305.05391/) (2023)
- [Privacy-Preserving Face Recognition Using Trainable Feature Subtraction](https://mlsanity.com/paper/2403.12457/) (2024)
- [Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters](https://mlsanity.com/paper/2201.12467/) (2022)
- [Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility](https://mlsanity.com/paper/2305.10994/) (2023)
- [Obfuscation of Images via Differential Privacy: From Facial Images to General Images](https://mlsanity.com/paper/2102.11072/) (2021)
- [Differentially Private Video Activity Recognition](https://mlsanity.com/paper/2306.15742/) (2023)
- [R2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions](https://mlsanity.com/paper/2009.09451/) (2020)
- [De-anonymization Attacks on Neuroimaging Datasets](https://mlsanity.com/paper/1908.03260/) (2019)

## Abstract (arXiv)

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.