Christopher Bryan
Arizona State University Campus Immersion, AZ·Computer Science
Charts and graphs are a main way society shares data. However, many datasets contain sensitive personal information.
- Fields
Computer Science
- Topics
data visualization
information visualization
data privacy
- Grants
- 1 known grant, last ended Sep 2026
What they work on
- Develop a visualization grammar, a way to specify chart designs without low-level programming, that supports privacy constraints and data sanitization actions.
- Run large-scale crowdsourced experiments to measure how chart design choices, privacy rules and sanitization techniques affect the usefulness and privacy of visualizations.
- Build open-source tools that recommend privacy-preserving visualizations and support authoring and refining them, based on the resulting design guidelines.
More about the research
Even when the raw data stay private, a published chart can leak details or let someone infer facts about individuals. The challenge is to make public visualizations that are both clear and safe.
Why it matters
Organizations that publish charts from sensitive data could use these tools to share useful graphics without exposing private information about individuals. Examples include health, government and business records. Open-source tools and design guidelines could make privacy-aware visualization practical for everyday analysts, not just privacy experts. Teaching materials could raise awareness of these risks among students.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Alexander Kale
University of Chicago, IL·Computer Science·Early-career
Develop a theory that defines visualization design goals as trade-offs among kinds of information loss, made computable through mathematical formalisms derived from example analysis, synthesis and expert interviews.
Khairi Reda
University of Illinois Chicago, IL·Computer Science·Early-career
Extend image-computable models of human texture perception to chart and map images, predicting which data properties the early-to-mid visual cortex retains for the viewer.
Michael Gleicher
University of Wisconsin-Madison, WI·Computer Science
Determine how people interpret small subsets of data items to grasp an overview of a large collection, using empirical user studies that link subset design choices to task performance.
The school
Arizona State University Campus Immersion: 36 labs on PI Pages, $1.0B a year on research, 625 research doctorates a year.
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