Personalized w-Event Privacy for Infinite Stream Estimation

2026-05-09

AI Summary

This work addresses the limitation of existing differential privacy mechanisms for streaming data, which typically assume uniform privacy requirements across all users and thus fail to accommodate individualized preferences. To overcome this, the paper proposes a personalized w-event differential privacy framework that introduces two key innovations: the (w,ℰ)-event-level personalized differential privacy ((w,ℰ)-EPDP) definition and the (τ,w_B,w_F)-event model. These enable users to dynamically specify their privacy parameters within sliding windows. The framework further incorporates a Personalized Window-based Sliding Mechanism (PWSM) and dynamic privacy budget allocation strategies (DPBD/DPBA), complemented by budget reservation, absorption, and borrowing mechanisms. This design rigorously preserves privacy while substantially enhancing the utility of statistical estimates. Experimental results demonstrate that the proposed approach reduces estimation error by at least 53.6% compared to the current state-of-the-art methods.

Abstract

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.

Problem

• personalized privacy
• w-event privacy
• infinite data streams
• differential privacy
• stream estimation

Innovation

• personalized differential privacy
• w-event privacy
• infinite data streams
• budget absorption
• dynamic privacy budgeting