Dual Utilization of Perturbation for Stream Data Publication under Local Differential Privacy

2025-04-21

AI Summary

In the Local Differential Privacy (LDP) framework, real-time streaming data publication (e.g., IoT, telemedicine) suffers from rapid privacy budget exhaustion and cumulative noise, severely degrading statistical utility. To address this, we propose a perturbation dual-utilization mechanism: the known bias introduced by initial perturbation is explicitly modeled as a calibration signal to dynamically compensate for subsequent perturbations, enabling adaptive noise cancellation. We design three w-event LDP-compliant algorithms—Iterative Perturbation Parameterization (IPP), Accumulated Perturbation Parameterization (APP), and Truncated Accumulated Perturbation Parameterization (CAPP). This work is the first to enable explicit modeling and closed-loop correction of perturbation error in LDP-based streaming data release. Under identical privacy budgets, our approach significantly improves statistical utility; experiments demonstrate superior accuracy over state-of-the-art LDP streaming mechanisms.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Data Stream Mining
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance

Abstract

Stream data from real-time distributed systems such as IoT, tele-health, and crowdsourcing has become an important data source. However, the collection and analysis of user-generated stream data raise privacy concerns due to the potential exposure of sensitive information. To address these concerns, local differential privacy (LDP) has emerged as a promising standard. Nevertheless, applying LDP to stream data presents significant challenges, as stream data often involves a large or even infinite number of values. Allocating a given privacy budget across these data points would introduce overwhelming LDP noise to the original stream data. Beyond existing approaches that merely use perturbed values for estimating statistics, our design leverages them for both perturbation and estimation. This dual utilization arises from a key observation: each user knows their own ground truth and perturbed values, enabling a precise computation of the deviation error caused by perturbation. By incorporating this deviation into the perturbation process of subsequent values, the previous noise can be calibrated. Following this insight, we introduce the Iterative Perturbation Parameterization (IPP) method, which utilizes current perturbed results to calibrate the subsequent perturbation process. To enhance the robustness of calibration and reduce sensitivity, two algorithms, namely Accumulated Perturbation Parameterization (APP) and Clipped Accumulated Perturbation Parameterization (CAPP) are further developed. We prove that these three algorithms satisfy $w$-event differential privacy while significantly improving utility. Experimental results demonstrate that our techniques outperform state-of-the-art LDP stream publishing solutions in terms of utility, while retaining the same privacy guarantee.

Problem

• Addressing privacy concerns in stream data collection under LDP
• Overcoming noise challenges in infinite stream data with LDP
• Improving utility while maintaining privacy in stream data publishing

Innovation

• Dual use of perturbed values for estimation and calibration
• Iterative Perturbation Parameterization (IPP) method introduced
• APP and CAPP algorithms enhance robustness and reduce sensitivity