# LDP-Slicing: Local Differential Privacy for Images via Randomized Bit-Plane Slicing

arXiv: https://arxiv.org/abs/2603.03711 · HTML: https://mlsanity.com/paper/2603.03711/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks](https://mlsanity.com/paper/1905.13545/) (2019)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Mixed Differential Privacy in Computer Vision](https://mlsanity.com/paper/2203.11481/) (2022)
- [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)
- [DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain](https://mlsanity.com/paper/2207.07340/) (2022)
- [Privacy-Preserving Face Recognition Using Random Frequency Components](https://mlsanity.com/paper/2308.10461/) (2023)
- [Differentially Private Imaging via Latent Space Manipulation](https://mlsanity.com/paper/2103.05472/) (2021)
- [LDP-Feat: Image Features with Local Differential Privacy](https://mlsanity.com/paper/2308.11223/) (2023)
- [A Fusion-Denoising Attack on InstaHide with Data Augmentation](https://mlsanity.com/paper/2105.07754/) (2021)

## Built on by (most cited first, 2 of 2)

- [Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes](https://mlsanity.com/paper/2607.03392/) (2026)
- [Private Vertical Federated Inference for Time-Series](https://mlsanity.com/paper/2605.08343/) (2026)

## Abstract (arXiv)

Local Differential Privacy (LDP) is the gold standard trust model for privacy-preserving machine learning by guaranteeing privacy at the data source. However, its application to image data has long been considered impractical due to the high dimensionality of pixel space. Canonical LDP mechanisms are designed for low-dimensional data, resulting in severe utility degradation when applied to high-dimensional pixel spaces. This paper demonstrates that this utility loss is not inherent to LDP, but from its application to an inappropriate data representation. We introduce LDP-Slicing, a lightweight, training-free framework that resolves this domain mismatch. Our key insight is to decompose pixel values into a sequence of binary bit-planes. This transformation allows us to apply the LDP mechanism directly to the bit-level representation. To further strengthen privacy and preserve utility, we integrate a perceptual obfuscation module that mitigates human-perceivable leakage and an optimization-based privacy budget allocation strategy. This pipeline satisfies rigorous pixel-level varepsilon-LDP while producing images that retain high utility for downstream tasks. Extensive experiments on face recognition and image classification demonstrate that LDP-Slicing outperforms existing DP/LDP baselines under comparable privacy budgets, with negligible computational overhead.