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Title: Recent advances in private synthetic data generation Authors:  Steven Wu - Carnegie Mellon University (United States) [presenting]
Abstract: The focus is on differentially private synthetic data---a privatized version of the dataset that consists of fake data records and that approximates the real dataset on important statistical properties of interest. We will present our recent results on private synthetic data that leverage practical optimization heuristics to circumvent the computational bottleneck in existing work. The techniques are motivated by a modular, game-theoretic framework, which can flexibly work with methods such as integer program solvers and deep generative models.