UC Davis

Differential privacy has seen a lot of growth in the last decade and has been accepted as a rigorous definition of privacy. A key use case of differential privacy is to generate synthetic data that can be released to the public without concerns about revealing sensitive information. Much of the research in differential privacy has focused on offline applications with the assumption that all data is available at once. When these algorithms are applied in practice to streams where data is collected over time, this either violates the privacy guarantees or results in poor utility.In this dissertation, we propose three methods that target the task of generating differentially private synthetic data for three different use cases. In Chapters 3 and 4 we look at streams in spatial and tabular spaces but with the assumption that the number of contributions by any particular user is limited. In Chapter 5 we consider the task of generating trajectories that preserve the correlation between points submitted by a user without restricting the number of contributions they make. We show that these proposed methods perform better than existing baselines and have the potential to be adopted for real-world datasets.