Differential privacy (DP) is a promising framework for privacy-preserving data science, but
recent studies have exposed challenges in bringing this theoretical framework for privacy …

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 …

As Differential Privacy (DP) transitions from theory to practice, visualization has surfaced as
a catalyst in promoting acceptance and usage. Despite the potential of visualization tools to …

Differential privacy (DP) has the potential to enable privacy-preserving analysis on sensitive
data, but requires analysts to judiciously spend a limited``privacy loss budget''$\epsilon …

Matrix reordering permutes the rows and columns of a matrix to reveal meaningful visual
patterns, such as blocks that represent clusters. A comprehensive collection of matrices …

Accessing data collected by federal statistical agencies is essential for public policy
research and improving evidence-based decision making, such as evaluating the …

Despite the potential of differentially private data visualization to harmonize data analysis
and privacy, research in this area remains relatively underdeveloped. Boxplots are a widely …

The cumulative distribution function (CDF) is fundamental due to its ability to reveal
information about random variables, making it essential in studies that require privacy …

Federated learning (FL) for distributed data has gained significant attention by enabling
model training on local data without transferring it to a central system. While this approach …

Privacy protection is important in visualization due to the risk of leaking personal sensitive
information. In this paper, we study the problem of privacy-preserving visualizations using …