Cooperative Local Differential Privacy: Securing Time Series Data in Distributed Environments

TL;DR

The paper addresses privacy for continuous time-series data in distributed environments by identifying weaknesses of traditional LDP when aggregating over time windows. It introduces Cooperative Local Differential Privacy (CLDP), which distributes sine-wave based noise across multiple users so that aggregated perturbations cancel while preserving the aggregated statistics. The method uses a complete sine wave partitioned among users, randomized noise samples, and shuffling to guarantee privacy with a formal security metric $P_{break}=(1/k)^{l\cdot u}$; it is validated on real and synthetic datasets, showing privacy-utility trade-offs as $k$, $l$, $u$, and $A$ vary. The results suggest CLDP scales to large, real-time time-series deployments, enabling secure multi-user aggregation with high data utility, while acknowledging limitations like stable participation and windowed assumptions.

Abstract

The rapid growth of smart devices such as phones, wearables, IoT sensors, and connected vehicles has led to an explosion of continuous time series data that offers valuable insights in healthcare, transportation, and more. However, this surge raises significant privacy concerns, as sensitive patterns can reveal personal details. While traditional differential privacy (DP) relies on trusted servers, local differential privacy (LDP) enables users to perturb their own data. However, traditional LDP methods perturb time series data by adding user-specific noise but exhibit vulnerabilities. For instance, noise applied within fixed time windows can be canceled during aggregation (e.g., averaging), enabling adversaries to infer individual statistics over time, thereby eroding privacy guarantees. To address these issues, we introduce a Cooperative Local Differential Privacy (CLDP) mechanism that enhances privacy by distributing noise vectors across multiple users. In our approach, noise is collaboratively generated and assigned so that when all users' perturbed data is aggregated, the noise cancels out preserving overall statistical properties while protecting individual privacy. This cooperative strategy not only counters vulnerabilities inherent in time-window-based methods but also scales effectively for large, real-time datasets, striking a better balance between data utility and privacy in multiuser environments.

Figures (7)

• Figure 1: Overall Architecture for Cooperative LDP
• Figure 2: Sine Wave Partitioning And Noise Distribution
• Figure 3: Perturbed VS Original Data
• Figure 4: MSE for Varying Tossing Space ($k$) vs. Numbers of Users ($u$)
• Figure 5: MSE for Varying Data Sample ($l$) vs. Numbers of Users ($u$)