Illuminating the Landscape of Differential Privacy: An Interview Study on the Use of Visualization in Real-World Deployments.

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 support differential privacy implementation, their development is limited by a lack of understanding of the overall deployment process, practitioner challenges, and the role of visual tools in real-world deployments. To narrow this gap, we interviewed 18 professionals from various backgrounds who regularly engage with differential privacy in their work.

Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications.

We present the results of a scientometric analysis of 30 years of IEEE VIS publications between 1990-2020, in which we conducted a multifaceted analysis of interdisciplinary collaboration and gender composition among authors. To this end, we curated BiblioVIS, a bibliometric dataset that contains rich metadata about IEEE VIS publications, including 3032 articles and 6113 authors. One of the main factors differentiating BiblioVIS from similar datasets is the authors' gender and discipline data, which we inferred through iterative rounds of computational and manual processes.

Designing Technology for Sociotechnical Problems: Challenges and Considerations.

Designing technology for sociotechnical problems is challenging due to the heterogeneity of stakeholders' needs, the diversity among their values and perspectives, and the disparity in their technical skills. Careful considerations are needed to ensure that data collection is inclusive and representative of the target populations. It is also important to employ data analysis methods that are compatible with users' technical skills and are capable of drawing a representative picture of people's values, priorities, and needs.

Investigating Visual Analysis of Differentially Private Data.

Differential Privacy is an emerging privacy model with increasing popularity in many domains. It functions by adding carefully calibrated noise to data that blurs information about individuals while preserving overall statistics about the population. Theoretically, it is possible to produce robust privacy-preserving visualizations by plotting differentially private data.

Embedded Merge & Split: Visual Adjustment of Data Grouping.

Data grouping is among the most frequently used operations in data visualization. It is the process through which relevant information is gathered, simplified, and expressed in summary form. Many popular visualization tools support automatic grouping of data (e.

Visualizing Dimension Coverage to Support Exploratory Analysis.

Data analysis involves constantly formulating and testing new hypotheses and questions about data. When dealing with a new dataset, especially one with many dimensions, it can be cumbersome for the analyst to clearly remember which aspects of the data have been investigated (i.e.