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Synthetic data has been hailed as the silver bullet for privacy preservingdata analysis. If a record is not real, then how could it violate a person'sprivacy? In addition, deep-learning based generative models are employedsuccessfully to approximate complex high-dimensional distributions from dataand draw realistic samples from this learned distribution. It is oftenoverlooked though that generative models are prone to memorising many detailsof individual training records and often generate synthetic data that tooclosely resembles the underlying sensitive training data, hence violatingstrong privacy regulations as, e.g., encountered in health care. Differentialprivacy is the well-known state-of-the-art framework for guaranteeingprotection of sensitive individuals' data, allowing aggregate statistics andeven machine learning models to be released publicly without compromisingprivacy. The training mechanisms however often add too much noise during thetraining process, and thus severely compromise the utility of these privatemodels. Even worse, the tight privacy budgets do not allow for many trainingepochs so that model quality cannot be properly controlled in practice. In thispaper we explore an alternative approach for privately generating data thatmakes direct use of the inherent stochasticity in generative models, e.g.,variational autoencoders. The main idea is to appropriately constrain thecontinuity modulus of the deep models instead of adding another noise mechanismon top. For this approach, we derive mathematically rigorous privacy guaranteesand illustrate its effectiveness with practical experiments.

Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders