Hide-and-Seek Privacy Challenge
James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul, Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave and, Mihaela van der Schaar

TL;DR
This paper discusses the NeurIPS 2020 Hide-and-Seek Privacy Challenge, which aims to develop synthetic clinical time-series data that balances data utility with privacy protection against re-identification attacks.
Contribution
It introduces a novel competition framework and dataset to advance generative models that preserve temporal dynamics while enhancing privacy in high-dimensional clinical data.
Findings
Development of high-quality synthetic time-series data
Improved methods for preventing patient re-identification
Benchmark results for privacy-preserving data generation
Abstract
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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Artificial Intelligence in Healthcare and Education · Cryptography and Data Security
