PUBLIC MARKS with search hidden

When we move into any social space we have to choose which of our faces to show. At any given moment, a few of them are visible. The others are hidden, often because we think they are somehow "weird".

A database of hidden settings for Mac OS X

Every time that you login into your local Unix-like machine or a remote hosting server through a FTP client to upload a file or use SSH to get your stuff done, you’re leaving behind a trail of evidence showing your online behaviour: where and when you log in, how often and how long your online sessions are, in short: your modus operandi. This visualization tool unveils this hidden data, which is gathered by running a few builtin UNIX commands and is analyzed onsite.

One of the 8 works I created for the Data Art Show at the Pink Hobo Gallery in Minneapolis.

All these pieces are a pun on the new craze for data visualization. The goals of data visualization as I understand them are to make complicated issues more understandable, to make obscured connections visible and to reveal hidden patterns in the data. After all these tasks have been solved ideally the result should be aesthetically pleasing as well.

But when I look around what is being done in data visualization today I have the suspicion that in many cases the design is more important than the actual information and that the use of data is more an excuse to justify the use of aesthetics.

Since I do not have a problem with aesthetics for their own sake in these pieces I deliberately took the opposite direction. Since I wanted to create something visually interesting I made up my own data which would give me the desired results. All these works are the result of generative algorithms, so all the elements and their connections are actually data and not something I assembled manually in Illustrator.

Data visualization is the use of tools to represent data in the form of charts, maps, tag clouds, animations, or any graphical means that make content easier to understand. Graphic representations of data are popular because they open up the way we think about data, reveal hidden patterns, and highlight connections among elements. Because current web applications allow anyone with access to data to enter information and easily create a virtualization of it, students, informal learners, and the purely curious can now easily create visualizations that might reveal trends that were not obvious from the numbers alone.

Computer scientists have recently undermined our faith in the privacy-protecting power of anonymization, the name for techniques for protecting the privacy of individuals in large databases by deleting information like names and social security numbers. These scientists have demonstrated they can often “reidentify” or “deanonymize” individuals hidden in anonymized data with astonishing ease. By understanding this research, we will realize we have made a mistake, labored beneath a fundamental misunderstanding, which has assured us much less privacy than we have assumed. This mistake pervades nearly every information privacy law, regulation, and debate, yet regulators and legal scholars have paid it scant attention. We must respond to the surprising failure of anonymization, and this Article provides the tools to do so.