Abstract
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.
Abstract (translated)
局部差异隐私（LDP）是保障数据源隐私的隐私保护机器学习中的黄金标准信任模型。然而，由于像素空间的高度维度性，长期以来认为将其应用于图像数据是不切实际的。传统的LDP机制针对低维数据设计，在应用于高维像素空间时会导致严重的效用损失。本文展示了这种效用损失并非源自LDP本身，而是源于其对不合适的数据表示的应用。我们引入了LDP-Slicing，这是一种轻量级、无需训练的框架，用于解决这一领域不匹配的问题。我们的关键洞察是将像素值分解为一系列二进制位平面。此转换允许我们将LDP机制直接应用于位级别的表示。为了进一步增强隐私并保持效用，我们整合了一个感知混淆模块以减轻人类可察觉的信息泄露，并采用了一种基于优化的隐私预算分配策略。该流程满足严格的像素级$\varepsilon$-LDP标准，同时生成具有高下游任务效用的图像。在人脸识别和图像分类方面的广泛实验表明，在相似的隐私预算下，LDP-Slicing优于现有的差分隐私（DP）/LDP基准方法，并且计算开销可以忽略不计。
URL
https://arxiv.org/abs/2603.03711
