Algorithms for synthetic data release under differential privacy
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Abstract
Releasing sensitive data while preserving privacy is an important problem that has attracted considerable attention in recent years. The state-of-the-art paradigm for addressing this problem is differential privacy, which offers a strong degree of privacy protection without making restrictive assumptions about the adversary. Most efforts to date to perform differentially private data release end up mired in complexity, overwhelm the signal with noise, and are not effective for use in practice. In this thesis, we introduce three novel solutions for complex data publication under differential privacy, namely, PrivBayes, PrivTree and the ladder framework. Compared to the previous work, our methods (i) enable the private release of a wide range of data types, i.e., multidimensional tabular data, spatial data, sequence data and graph data, (ii) improve the utility of released data by introducing significantly less perturbations in data modelling and (iii) are query-independent, such that many different queries (linear or non-linear) can be accurately evaluated on the same set of released data.
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