Differentially Private Small Dataset Release Using Random Projections


Small datasets form a significant portion of releasable data in high sensitivity domains such as healthcare. But, providing differential privacy for small dataset release is a hard task, where current state-of-the-art methods suffer from severe utility loss. As a solution, we propose DPRP (Differentially Private Data Release via Random Projections), a reconstruction based approach for releasing differentially private small datasets. DPRP has several key advantages over the state-of-the-art. Using seven diverse real-life datasets, we show that DPRP outperforms the current state-of-the-art on a variety of tasks, under varying conditions, and for all privacy budgets.

In Proceedings of the 36thConference on Uncertainty in Artificial Intelligence (UAI)
Lovedeep Gondara
Lovedeep Gondara
Research Scientist

My research interests include machine learning (deep learning) and statistics, with current research focus on large language models, differential privacy, and their applications to healthcare.