Eventos

Generating synthetic data is one of the key problems in private data analysis. In this talk, I will provide a summary of existing work, including well-established privacy attacks from the literature. After this introduction, I will present recent contributions on generating private synthetic data with relative accuracy guarantees (i.e, a mixture of additive and multiplicative error). Our main result provides (counting) error rates can be made poly-logarithmic in the sample size, data universe size, and the number of linear queries; a result which is provably unattainable in the purely-additive counterpart.