PRIVACY-PRESERVING VISUALIZATION OF BRAIN FUNCTIONAL NETWORK CONNECTIVITY

The connectogram is a commonly used visualization of brain functional network connectivity (FNC). In this paper we study the problem of privacy-preserving connectogram visualization using differential privacy. We investigate several approaches based on perturbing correlation values and characterize their privacy cost and the impact of pre- and post-processing. In order to obtain a better privacy/visual utility tradeoff, we propose a new workflow for connectogram visualization with privacy guarantees. This workflow successfully generates connectograms similar to their non-private counterparts for group comparisons. Experiments show that qualitative assessments can be preserved while guaranteeing privacy. These results show that differential privacy is a promising method for protecting sensitive information in data visualization for biomedical data.