Danah Boyd WWW keynote: Privacy and Publicity in Big Data

Today Danah Boyd's gave an address on the Privacy and Publicity in the context of big data at WWW 2010 . Danah released a crib sheet summary on her website, which you should read. Here are Michael's notes from the talk.

• Privacy concerns are everywhere
• Big data (Social data created
  by people) magnifies these concerns
  
  
• Data is cheap today
• Making sense of the data, however, is still hard
• Accessing and processing it an ethical way is not investigated


• Methodological issues
• Big data introduces more
  questions than answers
• Ethnography tries to answer
  some of these “why” questions
  
  
• Social Sciences Approach
  – 4 key points
• Bigger is not always better
• Not all data equal
• “What” != “Why”
• Be careful in interpretations


• Sampling
• The way you sample affects
  your results – hard to create truly random representative sample
• Big data doesn’t mean
  “the whole of the data”
• No matter how many tweets
  you have, your sample is always biased
• Oversampling users who tweet
  frequently
  
  
• Not all data are equal
• What does your network represent?
  Types of social network
• Articulated
• Behavioral
• Personal
  
  
• Data from Facebook is not
  necessarily more accurate that other social (smaller) network
• Facebook friends != person’s social network
• Frequency of conversation != personal closeness
• What != Why
• Correlation does not mean causation
• Even if your model points that there are two connected events doesn’t mean one causes
  the other
• Results need to be interpreted
• Technology can corrupt social
  science research by making simplifying assumptions and ignoring how
  the context in which original results were obtained


• Uncertainty principle applies
• Networks are made of people,
  not of abstract nodes on the graph
• Data in the network is about
  real people’s lives
  
  
• Just because data is accessible
  doesn’t mean that using it is ethical!
• Privacy is context
• Walls (Technology) have
  ears (and mouths)
  
  
• Five point for privacy security
• Security through obscurity
• Violated more and more by
  technology
• Technologies change people’s
  behavior
• Not all is meant to be publicized
• Do we all want to become
  “digital micro-celebrities” and fear the “digital paparazzi”?
• PII vs. PEI (Personal Identifiable
  vs. Embarrassing Information)
• Algorithms have a hard time
  discerning PII & PEI
• Data out of context is a
  privacy violation
• Privacy is not access control


• People care about privacy
• But they all also care about
  publicity – a right to be in public
  
  
• Facebook
• Facebook users have an impression
  that “Facebook is more private than MySpace”
• Newsfeed – publicizing
  implicit (but accessible) content in explicit way
• Initially controversial,
  became a great success
• Created a set of norms in
  the “Facebook world”
• Beacon – people are vessels
  for advertisements
• Was a failure, ended in
  a user lawsuit
• New default privacy settings
• Research shows that people
  do not understand their privacy settings in Facebook
• In fact, their mental map
  of settings doesn’t match the actual settings
• Slow changes from private
  to public
• Users are like frogs who
  are slowly “cooked” and do not realize it
• Data from 3^rd
  party sites is slowly aggregates
• Tastes, web actions
  are made public
• Opt-out is the norm at Facebook
• People do not understand
  what they implicitly agree to
  
  
• Regulations
• Involvement from governments
  (esp. from Europe,Canada)
• Researchers --- need to
  understand the consequences of their analysis