PETS: Inference-Time Differentially Private Synthetic Time Series Generation

Existing methods for differentially private (DP) synthetic time series generation inject privacy during model training via DP-SGD, requiring private data in the training phase, expensive hyperparameter tuning, and costly retraining for new domains. We propose Private Evolution for Time Series (PETS), the first inference-time framework for DP synthetic time series generation via Private Evolution (PE). In this setting, private data is not used to train generative models, but only to guide the selection of synthetic outputs at inference time, improving distributional fidelity while satisfying a privacy-budget constraint. PETS is modular by design, enabling modular adaptation without training on private data. Building on top of PE, we instantiate three concrete schemes, ChatTS+VAE, Diffusion-TS, and TimeCraft, each combining a generation module, a variation module, and a shared contrastive embedding for similarity-driven selection. Empirically, PETS achieves a mean $6.0\times$ lower C-FID than state-of-the-art (SOTA) DP methods across four public–private dataset pairs over $13$ privacy budgets ($\epsilon =0.125$ to $32$, plus $\infty$), with gains up to $17.6\times$ at $\epsilon =0.175$. For downstream tasks, PETS achieves a mean $1.4\times$ the activity classification accuracy of SOTA DP methods over these privacy budgets, showing PETS produces high-quality DP synthetic time series with strong utility-privacy trade-offs.