In applications such as event monitoring, log analysis, and video querying, $w$-event privacy protects individual data within a sliding time window while supporting accurate stream statistics. Existing studies on infinite data streams mainly assume homogeneous privacy requirements for all users, which cannot capture user-specific privacy preferences. This paper studies personalized $w$-event privacy for private data stream estimation. We first design the Personalized Window Size Mechanism (PWSM), which supports personalized privacy requirements at each time slot. Based on PWSM, we propose Personalized Budget Distribution (PBD) and Personalized Budget Absorption (PBA) to estimate streaming statistics under $\boldsymbol{w}$-Event $\boldsymbol{\mathcal{E}}$ Personalized Differential Privacy (($\boldsymbol{w}$, $\boldsymbol{\mathcal{E}}$)-EPDP). PBD guarantees that the budget reserved for the next time step is no smaller than the budget consumed in the previous release, while PBA improves the current budget by absorbing unused budgets from the previous $k$ time slots and borrowing from the next $k$ time slots. We further develop Dynamic Personalized Budget Distribution (DPBD) and Dynamic Personalized Budget Absorption (DPBA), which allow users to dynamically adjust privacy requirements while satisfying $(τ, \boldsymbol{w}_B, \boldsymbol{w}_F)$-Event $(\boldsymbol{\mathcal{E}}_B, \boldsymbol{\mathcal{E}}_F)$-Personalized Differential Privacy. We prove that all proposed methods achieve the corresponding personalized differential privacy guarantees and derive their error upper bounds. Experiments show that our methods reduce estimation error by at least $53.6\%$ compared with state-of-the-art algorithms.

翻译：在事件监控、日志分析和视频查询等应用中，$w$-事件隐私保护滑动时间窗口内的个体数据，同时支持准确的流统计。现有关于无限数据流的研究主要假设所有用户具有同质的隐私需求，这无法捕捉用户特定的隐私偏好。本文研究私有数据流估计中的个性化$w$-事件隐私。我们首先设计了个性化窗口大小机制（PWSM），该机制支持每个时间槽的个性化隐私需求。基于PWSM，我们提出了个性化预算分配（PBD）和个性化预算吸收（PBA），用于在$\boldsymbol{w}$-事件$\boldsymbol{\mathcal{E}}$个性化差分隐私（($\boldsymbol{w}$, $\boldsymbol{\mathcal{E}}$)-EPDP）下估计流统计量。PBD保证为下一个时间步预留的预算不小于上一次发布中消耗的预算，而PBA通过吸收前$k$个时间槽的未使用预算并从后$k$个时间槽借用预算来改进当前预算。我们进一步开发了动态个性化预算分配（DPBD）和动态个性化预算吸收（DPBA），允许用户在满足$(τ, \boldsymbol{w}_B, \boldsymbol{w}_F)$-事件$(\boldsymbol{\mathcal{E}}_B, \boldsymbol{\mathcal{E}}_F)$-个性化差分隐私的同时动态调整隐私需求。我们证明了所有提出的方法均能实现相应的个性化差分隐私保障，并推导了其误差上界。实验表明，与最先进算法相比，我们的方法将估计误差降低了至少$53.6\%$。