Differentially-Private Synthetic Datasets for Confidentiality Protection of Statistical Data

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"Your answers will be kept confidential and will only be used for statistical purposes." People often promise you this when they ask you for some personal information. Did you ever wonder how they make sure to respect their promise? How can they share parts of the dataset with others, or at least the results of the study, while protecting the confidentiality of your answers? My research program concerns differential privacy, a relatively new and very rigorous criterion to measure the confidentiality guarantee offered by a mechanism design to protect such data. I work on new methods to create completely synthetic datasets (you could think of them as fake datasets) which satisfy this confidentiality criteria. These synthetic datasets will ensure that no one can identify you or deduct your answers to the questions in the study, while being close enough to the real datasets to be useful for statistical analyses. I will also adapt usual statistical inference methods to the particularities of such synthetic datasets.