VIGIL: Variance-Informed Latent Guidance for Private Time-Series Generation

Hosting time series diffusion models on third-party servers offers ubiquitous client access. But sharing raw time series with servers raises privacy concerns. Prior art enables clients to work around by sharing latent embeddings instead. But such methods are inapplicable to conditional tasks such as imputation and forecasting, since the constraints are specified in the client's data space, which is inaccessible to the server. We propose VIGIL, a novel framework that enables privacy-preserving time-series diffusion by conditioning entirely in the latent space, with no observable time-series values leaving the client's premises. VIGIL has three phases: (1) latent anchor optimisation finds a plausible latent reference consistent with the observed data; (2) the anchor confidence map identifies the anchor positions corresponding to observed and missing positions in the data space; and (3) uses the anchor and the confidence map to steer generation via latent-guided sampling towards the constraints induced by the observed values. Results on five datasets spanning window lengths from 64 to 168 time steps demonstrate that VIGIL matches the performance of approaches that directly operate on raw data. Despite guiding in the latent space, VIGIL even improves on its data-space counterpart's similarity scores by 1.3% on average across 20 evaluation settings. Code available at https://anonymous.4open.science/r/Synthetic-Time-Series-Generation/