We study the design of a privatization mechanism and privacy accounting in the Pufferfish Privacy (PP) family. Specifically, motivated by the curse of dimensionality and lack of practical composition tools for iterative learning in the recent Renyi Pufferfish Privacy (RPP) framework, we propose Sliced Renyi Pufferfish Privacy (SRPP). SRPP preserves PP/RPP semantics (customizable secrets with probability-aware secret-dataset relationships) while replacing high-dimensional Renyi divergence with projection-based quantification via two sliced measures, Average SRPP and Joint SRPP. We develop sliced Wasserstein mechanisms, yielding sound SRPP certificates and closed-form Gaussian noise calibration. For iterative learning systems, we introduce an SRPP-SGD scheme with gradient clipping and new accountants based on History-Uniform Caps (HUC) and a subsampling-aware variant (sa-HUC), enabling decompose-then-compose privatization and additive composition under a common slicing geometry. Experiments on static and iterative privatization show that the proposed framework exhibits favorable privacy-utility trade-offs, as well as practical scalability.

翻译：本研究致力于河豚隐私框架下的隐私化机制设计与隐私核算方法。针对近期Rényi河豚隐私框架中存在的维度灾难问题以及迭代学习场景下实用组合工具的缺失，我们提出了切片化Rényi河豚隐私框架。该框架在保持河豚隐私/Rényi河豚隐私语义特征（支持可定制的敏感信息定义及概率感知的敏感信息-数据集关联关系）的同时，通过两种切片化度量指标——平均切片化Rényi河豚隐私与联合切片化Rényi河豚隐私，将高维Rényi散度替换为基于投影的量化方法。我们开发了切片化Wasserstein机制，该机制能够提供可靠的切片化Rényi河豚隐私认证并实现闭式高斯噪声校准。针对迭代学习系统，我们提出了结合梯度裁剪技术的切片化Rényi河豚隐私随机梯度下降方案，并基于历史均匀上限及其子采样感知变体构建了新型隐私核算器，使得在统一切片几何结构下能够实现"先分解后组合"的隐私化处理与加性组合。静态与迭代隐私化实验表明，所提框架在隐私-效用权衡方面表现优异，并具备良好的实际可扩展性。