This paper introduces personalized mechanisms for estimating streaming statistics under $w$-event personalized differential privacy, significantly improving accuracy compared to existing methods.
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.
PAC-DP: Personalized Adaptive Clipping for Differentially Private Federated Learning
The paper proposes PAC-DP, a personalized adaptive clipping framework that dynam…
PrivFedTalk: Privacy-Aware Federated Diffusion with Identity-Stable Adapters for Personalized Talkin…
PrivFedTalk introduces a privacy-aware federated framework for personalized talk…
TADP-RME: A Trust-Adaptive Differential Privacy Framework for Enhancing Reliability of Data-Driven S…
TADP-RME introduces a trust-adaptive differential privacy framework that enhance…
Differential Privacy in Generative AI Agents: Analysis and Optimal Tradeoffs
This paper develops a differential privacy framework to analyze and optimize pri…
Sequential Change Detection for Multiple Data Streams with Differential Privacy
The paper proposes DP-SUM-CUSUM, a differentially private method for detecting s…
Text-Based Personas for Simulating User Privacy Decisions
The paper introduces Narriva, a method that generates text-based synthetic priva…
Differentially Private Manifold Denoising
The paper introduces a differentially private manifold denoising framework that…
Answering Counting Queries with Differential Privacy on a Quantum Computer
This paper develops and analyzes two differentially private methods for answerin…