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：针对时间关联数据上的差分隐私流数据发布问题已有广泛研究，但如何在隐私性与效用性之间取得良好平衡仍是一个未解难题。现有研究聚焦于在关联数据上应用差分隐私时增强隐私保护，指出差分隐私在时间关联条件下可能面临额外的隐私泄露风险，因此不得不采用较小的隐私预算，导致效用性下降。本文提出一种基于后处理的框架，旨在提升时间关联条件下差分隐私数据发布的效用性。我们将该问题建模为给定发布的差分隐私数据与关联模型下的最大后验估计，并将其转化为非线性约束规划。在合成数据集上的实验表明，在严格隐私预算约束下，所提方法能使差分隐私数据的均方误差指标提升近两个数量级，显著改善数据效用性与准确性。