  The clinical time-series setting poses a unique combination of challenges to
data modeling and sharing. Due to the high dimensionality of clinical time
series, adequate de-identification 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, in the sense that new sequences respect the
original relationships between high-dimensional variables across time. From the
privacy perspective, the model should prevent patient re-identification by
limiting vulnerability to membership inference attacks. The NeurIPS 2020
Hide-and-Seek Privacy Challenge is a novel two-tracked competition to
simultaneously accelerate progress in tackling both problems. In our
head-to-head format, participants in the synthetic data generation track (i.e.
"hiders") and the patient re-identification track (i.e. "seekers") are directly
pitted against each other by way of a new, high-quality intensive care
time-series dataset: the AmsterdamUMCdb dataset. Ultimately, we seek to advance
generative techniques for dense and high-dimensional temporal data streams that
are (1) clinically meaningful in terms of fidelity and predictivity, as well as
(2) capable of minimizing membership privacy risks in terms of the concrete
notion of patient re-identification.
