The release of differentially private streaming data has been extensively studied, yet striking a good balance between privacy and utility on temporally correlated data in the stream remains an open problem. Existing works focus on enhancing privacy when applying differential privacy to correlated data, highlighting that differential privacy may suffer from additional privacy leakage under correlations; consequently, a small privacy budget has to be used which worsens the utility. In this work, we propose a post-processing framework to improve the utility of differential privacy data release under temporal correlations. We model the problem as a maximum posterior estimation given the released differentially private data and correlation model and transform it into nonlinear constrained programming. Our experiments on synthetic datasets show that the proposed approach significantly improves the utility and accuracy of differentially private data by nearly a hundred times in terms of mean square error when a strict privacy budget is given.

翻译：差分隐私流数据的发布已得到广泛研究，但在流数据中针对时序相关数据实现隐私与效用的良好平衡仍是一个开放问题。现有工作聚焦于在将差分隐私应用于相关数据时增强隐私保护，指出差分隐私在相关性条件下可能遭受额外的隐私泄露；因此必须采用较小的隐私预算，而这会恶化数据效用。本文提出一种基于后处理的框架，用于提升时序相关条件下差分隐私数据发布的效用。我们将该问题建模为给定已发布差分隐私数据及相关性模型下的最大后验估计，并将其转化为非线性约束规划问题。基于合成数据集的实验表明，在严格隐私预算约束下，所提方法能够将差分隐私数据的均方误差降低近两个数量级，显著提升数据效用与精度。