Privacy-Aware Time Series Synthesis via Public Knowledge Distillation

TL;DR

This paper introduces Pub2Priv, a framework that uses public knowledge and self-attention to generate privacy-preserving synthetic time series data, improving privacy-utility balance in sensitive domains.

Contribution

The paper proposes a novel public knowledge distillation approach with a self-attention mechanism for better privacy-aware time series synthesis.

Findings

Outperforms state-of-the-art benchmarks in privacy-utility trade-offs

Effective across finance, energy, and commodity trading domains

Introduces a practical privacy assessment metric

Abstract

Sharing sensitive time series data in domains such as finance, healthcare, and energy consumption, such as patient records or investment accounts, is often restricted due to privacy concerns. Privacy-aware synthetic time series generation addresses this challenge by enforcing noise during training, inherently introducing a trade-off between privacy and utility. In many cases, sensitive sequences is correlated with publicly available, non-sensitive contextual metadata (e.g., household electricity consumption may be influenced by weather conditions and electricity prices). However, existing privacy-aware data generation methods often overlook this opportunity, resulting in suboptimal privacy-utility trade-offs. In this paper, we present Pub2Priv, a novel framework for generating private time series data by leveraging heterogeneous public knowledge. Our model employs a self-attention mechanism to encode public data into temporal and feature embeddings, which serve as conditional inputs for a diffusion model to generate synthetic private sequences. Additionally, we introduce a practical metric to assess privacy by evaluating the identifiability of the synthetic data. Experimental results show that Pub2Priv consistently outperforms state-of-the-art benchmarks in improving the privacy-utility trade-off across finance, energy, and commodity trading domains.