The proliferation of streaming data analytics in data-driven applications raises critical privacy concerns, as directly collecting user data may compromise personal privacy. Although existing $w$-event local differential privacy (LDP) mechanisms provide formal guarantees without relying on trusted third parties, their practical deployment is hindered by two key limitations. First, these methods are designed primarily for publishing simple statistics at each timestamp, making them inherently unsuitable for complex queries. Second, they handle data at each timestamp independently, failing to capture temporal correlations and consequently degrading the overall utility. To address these issues, we propose MTSP-LDP, a novel framework for \textbf{M}ulti-\textbf{T}ask \textbf{S}treaming data \textbf{P}ublication under $w$-event LDP. MTSP-LDP adopts an \emph{Optimal Privacy Budget Allocation} algorithm to dynamically allocate privacy budgets by analyzing temporal correlations within each window. It then constructs a \emph{data-adaptive private binary tree structure} to support complex queries, which is further refined by cross-timestamp grouping and smoothing operations to enhance estimation accuracy. Furthermore, a unified \emph{Budget-Free Multi-Task Processing} mechanism is introduced to support a variety of streaming queries without consuming additional privacy budget. Extensive experiments on real-world datasets demonstrate that MTSP-LDP consistently achieves high utility across various streaming tasks, significantly outperforming existing methods.

翻译：在数据驱动应用中，流数据分析的普及引发了严重的隐私担忧，因为直接收集用户数据可能损害个人隐私。尽管现有的 $w$-事件本地差分隐私机制提供了不依赖可信第三方的形式化保证，但其实际部署受到两个关键限制的阻碍。首先，这些方法主要设计用于在每个时间戳发布简单统计量，本质上不适合处理复杂查询。其次，它们独立处理每个时间戳的数据，未能捕捉时序相关性，从而导致整体效用下降。为解决这些问题，我们提出了 MTSP-LDP，一种在 $w$-事件 LDP 下的**多任务流数据发布**新框架。MTSP-LDP 采用一种**最优隐私预算分配**算法，通过分析每个窗口内的时序相关性来动态分配隐私预算。随后构建一个**数据自适应的私有二叉树结构**以支持复杂查询，并通过跨时间戳分组与平滑操作进一步优化该结构以提升估计精度。此外，框架引入了一种统一的**免预算多任务处理**机制，可在不消耗额外隐私预算的前提下支持多种流查询。在真实数据集上的大量实验表明，MTSP-LDP 在各类流任务中均能持续实现高数据效用，显著优于现有方法。