Privacy-Aware Time Series Synthesis via Public Knowledge Distillation

• URL: http://arxiv.org/abs/2511.00700v1
• Date: Sat, 01 Nov 2025 20:44:24 GMT
• Title: Privacy-Aware Time Series Synthesis via Public Knowledge Distillation
• Authors: Penghang Liu, Haibei Zhu, Eleonora Kreacic, Svitlana Vyetrenko,
• Abstract summary: Pub2Priv is a novel framework for generating private time series data by leveraging heterogeneous public knowledge.<n>Our model employs a self-attention mechanism to encode public data into temporal and feature embeddings.<n>We introduce a practical metric to assess privacy by evaluating the identifiability of the synthetic data.
• Score: 5.510212613486574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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