GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces Anonymous Author(s) Affiliation Address email

We focus on the problem of generating high-quality, private synthetic glucose1 traces, a task generalizable to many other time series sources. Existing methods for2 time series data synthesis, such as those using Generative Adversarial Networks3 (GANs), are not able to capture the innate characteristics of glucose data and cannot4 provide any formal privacy guarantees without severely degrading the utility of the5 synthetic data. In this paper we present GlucoSynth, a novel privacy-preserving6 GAN framework to generate synthetic glucose traces. The core intuition behind our7 approach is to conserve relationships amongst motifs (glucose events) within the8 traces, in addition to temporal dynamics. Our framework incorporates differential9 privacy mechanisms to provide strong formal privacy guarantees.