Privacy
Data, Data Everywhere The Need for BIG Privacy in a World of Big Data by Ann Cavoukian #FOCI14
Ann Cavoukian, Ph.D., is the Information and Privacy Commissioner of Ontario, Canada. This morning, she gave a talk entitles "Data, Data Everywhere: The Need for BIG Privacy in a World of Big Data." Given that I love all things related to privacy, ethics, and standards, this talk was of great interest to me. Here are some of the key points that Ann addressed.
- - big data and privacy are complementary interests
- - her take, "privacy by design" is a win win proposition
- - www.privacybydesign.ca
- - privacy = personal control, freedom of choice, informational self-determination, context is key
- - in 2010, this landmark resolution was passed to preserve the future of privacy, and has been translated into 36 languages because people are so desperate for this information
- - the 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 -
- 1. be proactive not reactive, prevention not remedial
- 2. default condition needs to be privacy
- 3. privacy embedded into design
- 4. full functionality, positive sum not zero sum
- 5. end to end security, full lifecycle protection, from the outset, from collection to destruction at the end
- 6. visibility and transparency, keep it open, tell customers what you're doing, don't let them learn afterwar
- 7. respect for use privacy, keep it user centric
- - Big data will rule the world ' during the 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 things like data sampling
- - Forget big data, what is needed is good data
- - data analytics on context free data will only yield correlations, if you add context, then you might be able to impute causality
- - once businesses 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
- - 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
Annie Pettit, PhD is the Chief Research Officer at Peanut Labs, a company specializing in self-serve panel sample. Annie is a methodologist focused on data quality, listening research, and survey methods. She won Best Methodological Paper at Esomar 2013, and the 2011 AMA David K. Hardin Award. Annie tweets at @LoveStats and can be reached at annie@peanutlabs.com.