UC Irvine

In this thesis, we study privacy in the context of Decision Support(DS) applications. DS applications utilize data collected from numerous sources to guide important decisions. However, such applications could face severe privacy challenges if the data contains sensitive information about individuals. While techniques such as differential privacy are suited for privacy-preserving data sharing, their usefulness in the context of decision support (DS) applications is limited due to privacy and utility trade-offs as these techniques do not offer any guarantees on the quality of results. DS tasks, in contrast, require guarantees on the output quality to avoid making misleading and inaccurate decisions.

We explore the concept of minimally invasive data exploration for decision support that attempts to minimize privacy loss while supporting bounded guarantees on accuracy. We build a demo application and conduct empirical studies to understand privacy utility trade-offs of different privacy techniques and to highlight the need for accuracy driven privacy preserving data analysis. We formally define decision support queries and their accuracy requirements and present privacy preserving algorithms to answer these queries that minimize the privacy loss while providing the required accuracy guarantees for decision support.