Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification

The clinical time-series setting poses a unique combination of challenges to data modelling and sharing. Due to the high dimensionality of clinical time series, adequate deidentification to preserve privacy while retaining data utility is difficult to achieve using common de-identification techniques. An innovative approach to this problem is synthetic data generation. From a technical perspective, a good generative model for time-series data should preserve temporal dynamics; new sequences should respect the original relationships between high-dimensional variables across time. From the privacy perspective, the model should prevent patient re-identification. The NeurIPS 2020 Hide-and-Seek Privacy Challenge was a novel two-tracked competition to simultaneously accelerate progress in tackling both problems. In our head-to-head format, participants in the generation track ("hiders") and the patient re-identification track ("seekers") were directly pitted against each other by way of a new, high-quality intensive care time-series dataset: the Amsterda-