TedX Bay Area Talk: The Myth of Selective Sharing – Digital Health Futures: Empowerment or coercion?

I spoke about my concerns with the continued belief in selective sharing.  I argue at this TedX Bay Area talk that it is unwise to expect that digital information systems are capable of privacy or selective sharing.  In other words, it is a dangerous myth to believe in a feature that in practice fails regularly and by design.  In fact, it seems that it is practically impossible to create any digital information system that is secure.

In such a world we may want to reconsider our sharing practices, particularly if they were built on the idea of selective sharing.  If any of your digital information is something you would rather not share publicly, you may want to rethink the idea that you can keep your information private.

If you are building an information system, you may want to rethink the idea that you can offer selective sharing in a reliable form.

Thanks to the folks at TedX Bay Area, particularly Tatyana Kanzaveli for the opportunity to work out these thoughts and share them.

Here are the slides that were used in the talk:

NodeXL – a sample of user goals and research interests as a Wordle cloud

NodeXL - a sample of user goals and research interests as a Wodle cloud
NodeXL - a sample of user goals and research interests as a Wordle cloud

Thanks to inspiration from Adam Perer, here is a Wordle.net picture of some of feedback text — a rough presentation of the ideas, topics and research goals submitted by a collection of attendees of a recent lecture on NodeXL at Harvard.  A recording of the slides and audio from the talk is available from the Harvard Program on Networked Governance website and also on the Government Innovators Network website.

A copy of the video that does not require registration is available here.

As people registered for the lecture some took the time to share their interests in network analysis and the NodeXL project.  The key themes and topics from these statements focus on studying the social network of various populations of interest including (in no particular order): governments, enterprises, migrants, terrorists, innovators, politicians, corporations, criminals, research literature, sick people and online communities.

Research goals were focused on tasks like:

  • Map a complex set of relationships
  • Find key people: bridge roles, sinks, sources, etc..
  • Find “missing people”
  • Measure cohesion of subgroups
  • Map diffusion and change overtime
  • Find network correlates of the adoption of innovation

This feed back will shape the kinds of features we explore in the NodeXL project.  What are your goals for exploring network data sets?  What questions are you trying to answer?  What kinds of network structures are of greatest interest?  How can NodeXL best support that exploration?

Your comments welcome!