Data, Data Everywhere The Need for BIG Privacy in a World of Big Data by Ann Cavoukian, Ph.D., Information and Privacy Commissioner of Ontario, Canada #FOCI14 #MRX #GreatTalk

Live blogging from the #FOCI14 conference in Universal City. Any errors or bad jokes are my own.foci14

8:45 KEYNOTE Data, Data Everywhere The Need for BIG Privacy in a World of Big Data 
Ann Cavoukian, Ph.D., Information and Privacy Commissioner of ONTARIO, CANADA

  • big data and privacy are complementary interests
  • privacy by design is a win win proposition
  • if you don’t address privacy concerns, there will be a backlash
  • privacy = personal control, freedom of choice, informational self-determination, context is key
  • in 2010, passed this landmark resolution to preserve the future of privacy, has been translated into 36 languages because people are so desperate for this information
  • essence of it is to change the emphasis from a win-lose model to a win-win model, replace ‘vs’ with ‘and’
  • you must address privacy at the beginning of a program, embed it into the code at the beginning
  • 7 principles –
    • be proactive not reactive, prevention not remedial
    • default condition needs to be privacy
    • privacy embedded into design
    • full functionality, positive sum not zero sum
    • end to end security, full lifecycle protection, from the outset, from collection to destruction at the end
    • visibility and transparency, keep it open, tell customers what you’re doing, don’t let them learn afterwar
    • respect for use privacy, keep it user centric
  • Big data will rule the world – honeymoon phase, everything else must step aside, forget causality, correlation is enough
  • Then the honeymoon phase ends – found data… digital exhaust of web searches, credit card payments, mobiles pinging the nearest phone mast; these datasets are cheap to collect but they are messy and collected for disparate purposes
  • Big data is now in the trough of disillusionment
  • Google flu trends used to work and now doesn’t because Google engineers weren’t interested in context but rather selecting statistical patterns in the data – correlation over causation, a common assumption in big data analysis, imputed causality which is incorrect
  • MIT professor Alex Pentland has proposed a New Deal on Data – individuals to own their data and control how it is used and distributed
  • data problems don’t disappear just because you are working with big data instead of small data, you can’t just forget about data sampling
  • Forget big data, what is needed is good data
  • data analytics on context free data will only yield correlations, add context and then you might be able to impute causality
  • once business have amassed the personal information, it can be hard if not impossible for individuals to know how it will be used in the future – “A long way to privacy safeguards” New York Times Editorial
  • privacy is not a religion – if you want to do nothing, you can do nothing. but let people choose to do something
  • people now have to resign when data breaches happen, you must address them at the beginning
  • privacy should be treated as a business issue, not a compliance issue. gain a competitive advantage by claiming privacy, lead with it
  • proactive costs money but reactive costs lawsuits, brand damage, loss of trust, loss of consumer confidence
  • privacy drives innovation and creativity
  • privacy is a sustainable competitive advantage

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