Statistical framework and methodology for risk and privacy in complex and high-dimensional data

Modern data collection and storage results in complex and high-dimensional databases: they include a large number of variables, with a lot of interactions. At this same time, access and release of information that is, or is derived from, personal information involves complex challenges in terms of the potential for inappropriate disclosure (e.g., identification).
In this project we propose to develop a statistical methodology that can inform the evaluation of privacy assurances while preserving the statistical utility of complex, high-dimensional health data. The important themes of this work include high-dimensionality, sparsity and complexity.

Yizhen Teng;Chang Qu
Faculty Supervisor: 
Rafal Kulik
Partner University: