Data visualization is the backbone of exploratory analysis and storytelling for data scientists. The graphs not only provide tangible information from raw data but also stoke the interest of the audience to tell a convincing story. How else would you convince your clients, or back your decisions without relevant visualizations at hand?
However when it comes to the visualization of sensitive data, the goal is to intentionally hide some information to prevent unauthorized disclosure. Since visualization entails inherent information loss and other types of uncertainty in the screen space, these can be exploited for privacy-preserving purposes. In agreement to the excerpt above, what the privacy-assuring visualizations show, is a mix of truth and suppressed truth. Such visualizations should do justice to your storytelling, yet prevent unintended information leaks. Now we can see that data visualization has also assumed a significant role to provide privacy provisions as well.
Recent history has seen many public embarrassments and controversies caused by data breaches. Privacy preservation is not only an add-on but has become one of the most important requirements for services and applications. I propose to present the application of data visualization as a privacy preservation means, the associated metrics, and the challenges that make the concept more exciting.