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Privacy by Design Seminar - Jan 22, 2015

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Ryerson’s Privacy and Big Data Institute's inaugural seminar on Privacy by Design (PbD), the revolutionary privacy framework created by Dr. Ann Cavoukian which was unanimously passed as an international privacy standard in 2010 (translated into 37 languages). Dr. Cavoukian is now the Executive Director of the Privacy and Big Data Institute at Ryerson, and formerly served as the Information and Privacy Commissioner of Ontario for three terms. Dr. Cavoukian gave a presentation on Privacy by Design and its application to big data analytics, followed by a Q&A session.

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Privacy by Design Seminar - Jan 22, 2015

  1. 1. Ann Cavoukian, Ph.D.Ann Cavoukian, Ph.D. Executive Director Privacy and Big Data Institute Ryerson University Welcome to Privacy and Big Data Analytics – by Design Privacy by Design Seminar January 22, 2015
  2. 2. Presentation Outline 1.Privacy = Personal Control 2.Privacy is Essential to Freedom 3. Lead with Privacy by Design 4.Big Data Analytics 5.Privacy is Good for Business 6.SmartData 7.Concluding Thoughts
  3. 3. Privacy ≠ Secrecy Privacy is not about having something to hide
  4. 4. Privacy = Control
  5. 5. Privacy = Personal Control •User control is critical •Freedom of choice •Informational self-determination Context is key!
  6. 6. Privacy is Essential to Freedom: A Necessary Condition for Societal Prosperity and Well-Being • Innovation, creativity, and the resultant prosperity of a society requires freedom; • Privacy is the essence of freedom: Without privacy, individual human rights, property rights and civil liberties – the conceptual engines of innovation and creativity, could not exist in a meaningful manner; • Surveillance is the antithesis of privacy: A negative consequence of surveillance is the usurpation of a person’s limited cognitive bandwidth, away from innovation and creativity.
  7. 7. The Decade of Privacy by Design
  8. 8. Change the Paradigm to Positive-Sum, NOT Zero-Sum The Future of Privacy: Be Proactive
  9. 9. Landmark Resolution Passed to Preserve the Future of Privacy By Anna Ohlden – October 29th 2010 - http://www.science20.com/newswire/landmark_resolution_passed_preserve_future_privacy JERUSALEM, October 29, 2010 – A landmark Resolution by Ontario's Information and Privacy Commissioner, Dr. Ann Cavoukian, was approved by international Data Protection and Privacy Commissioners in Jerusalem today at their annual conference. The resolution recognizes Commissioner Cavoukian's concept of Privacy by Design - which ensures that privacy is embedded into new technologies and business practices, right from the outset - as an essential component of fundamental privacy protection. Full Article: http://www.science20.com/newswire/landmark_resolution_passed_preserve_future_privacy Adoption of “Privacy by Design” as an International Standard
  10. 10. 1. English 2. French 3. German 4. Spanish 5. Italian 6. Czech 7. Dutch 8. Estonian 9. Hebrew 10.Hindi 11.Chinese 12.Japanese 13. Arabic 14. Armenian 15. Ukrainian 16. Korean 17. Russian 18. Romanian 19. Portuguese 20. Maltese 21. Greek 22. Macedonian 23. Bulgarian 24. Croatian 25. Polish 26. Turkish 27. Malaysian 28. Indonesian 29. Danish 30. Hungarian 31. Norwegian 32. Serbian 33. Lithuanian 34. Farsi 35. Finnish 36. Albanian 37. Catalan Privacy by Design: Proactive in 37 Languages!
  11. 11. Privacy by Design’s Greatest Strength – Positive-Sum: The Power of “And” Change the paradigm from the dated zero-sum (win/win) to a “positive-sum” model: Create a win/win scenario, not an either/or (vs.) involving unnecessary trade-offs and false dichotomies … replace “vs.” with “and”
  12. 12. Privacy by Design: The 7 Foundational Principles 1. Proactive not Reactive: Preventative, not Remedial; 2. Privacy as the Default setting; 3. Privacy Embedded into Design; 4. Full Functionality: Positive-Sum, not Zero-Sum; 5. End-to-End Security: Full Lifecycle Protection; 6. Visibility and Transparency: Keep it Open; 7. Respect for User Privacy: Keep it User-Centric.
  13. 13. “Privacy by Design is considered one of the most important concepts by members of the Japanese Information Processing Development Center … We have heard from Japan’s private sector companies that we need to insist on the principle of Positive-Sum, not Zero-Sum and become enlightened with Privacy by Design.” — Tamotsu Nomura, Japan Information Processing Development Center, May 28, 2014 Letter from JIPDEC – May 28, 2014
  14. 14. Operationalizing Privacy by Design 9 PbD Application Areas •CCTV/Surveillance cameras in mass transit systems; •Biometrics used in casinos and gaming facilities; •Smart Meters and the Smart Grid; •Mobile Communications; •Near Field Communications; •RFIDs and sensor technologies; •Redesigning IP Geolocation; •Remote Home Health Care; •Big Data and Data Analytics.
  15. 15. Do NOT focus exclusively on the “uses” of personal data Zero-Sum Prevails: Let’s Change the Paradigm
  16. 16. http://www.privacybydesign.ca/index.php/paper/unintended-consequences-privacy-paternalism/
  17. 17. Privacy Paternalism “ Leaving it up to companies and governments to determine the acceptable secondary uses of personal data is a flawed proposition, that will no doubt lead to greater privacy infraction. If the history of privacy has taught us anything, it is that an individual’s loss of control over their personal data leads to greater privacy abuses, not fewer.” Cavoukian, Dix, and El-Emam
  18. 18. The Veil of Privacy “A regime that only pays attention to use erects a Potemkin Village of privacy. From a distance, it looks sound. But living within it we will find no shelter from the sun or rain.” – Professor Chris Hoofnagle The Potemkinism of Privacy Pragmatism Slate.com http://www.slate.com/articles/technology/future_tense/2014/09/data_use_ regulation_the_libertarian_push_behind_a_new_take_on_privacy.html
  19. 19. Privacy Paternalism “The authors fully agree that accountability should be strengthened, but disagree with the proposal to weaken critical FIPPs and diminishing the role of the individual … Diminishing limits on specified purposes, collection and uses of personal data minimizes rather than strengthens accountability.” Cavoukian, Dix, and El-Emam
  20. 20. OECD Privacy Principles (Fair Information Practices) 1. Collection Limitation 2. Data Quality 3. Purpose Specification 4. Use Limitation 5. Security Safeguards 6. Openness 7. Individual Participation 8. Accountability Revised July, 2013
  21. 21. Big Data
  22. 22. Big Data • 90% of all data was created within the last 2 years; • Big Data analysis and data analytics promise new opportunities to gain valuable insights and benefits – new predictive modes of analysis; • But, it will also enable expanded surveillance, increasing the risk of unauthorized use and disclosure, on a scale previously unimaginable.
  23. 23. First, Comes the Hype
  24. 24. The Hype Phase: • Big Data will rule the world! • Everything else (including privacy) must step aside; • Forget causality; correlation is enough.
  25. 25. Then, the Hype Doesn’t Deliver
  26. 26. Big Data Technology is Not Foolproof “Despite rampant interest from enterprise leaders and often sizeable investments in Big Data technologies, many programs still sputter or fail completely.” — Evanta Leadership Network, May 29, 2014.
  27. 27. Some People are Now Asking: Is Big Data a Big Mistake? • The Big Data that interests many companies is what we might call “found data” – the digital exhaust of web searches, credit card payments and mobiles pinging the nearest phone mast; • Such data sets are cheap to collect relative to their size – a messy collage of data-points, collected for disparate purposes; • But, how good is the data? — www.ft.com April 7, 2014
  28. 28. Big Data is moving from its “inflated expectations” phase to a “trough of disillusionment.” — Gartner Hype Cycle, April, 2014
  29. 29. MIT Big Data Expert Calls for Privacy “MIT Professor Alex Pentland has proposed a ‘New Deal on Data,’ which calls for individuals to own their data and control how it is used and distributed.” — Measuring Idea Flows to Accelerate Innovation, New York Times, April 15, 2014.
  30. 30. “But while big data promise much to scientists, entrepreneurs and governments, they are doomed to disappoint us if we ignore some very familiar statistical lessons. There are a lot of small data problems that occur in big data. They don’t disappear because you’ve got lots of the stuff … they get worse!” — David Spiegelhalter, Winton Professor, Cambridge University — Big data: are we making a big mistake? FT Magazine, March 2014. Quantity Does Not Equal Quality
  31. 31. “Forget Big Data … what is needed is Good Data” — Barrie McKenna, The serious economic cost of Canada's data deficit, Globe and Mail, May 12, 2014
  32. 32. 2013 Data Scientists Conference 88% of the Data Scientists surveyed said that consumers should worry about the privacy issues associated with Big Data - JSM 2013 Conference
  33. 33. Context is Key • Performing data analytics on context-free data will only yield correlations (which at times, will be spurious); • By adding context as a feature in the analytics, we may be able to impute causality – which has the potential to be invaluable in our analyses.
  34. 34. Privacy Breeds Innovation: It Does NOT Stifle It! • The argument that privacy stifles innovation reflects a dated, zero-sum mindset; • The notion that privacy must be sacrificed for innovation is a false win/lose dichotomy, consisting of unnecessary trade-offs; • The opposite is true – privacy drives innovation – it forces innovators to think creatively to find solutions that will serve multiple functionalities; • We need to abandon zero-sum thinking and adopt a positive-sum paradigm where both innovation and privacy may be achieved – we need a new playbook!
  35. 35. Privacy by Design and the Internet Engineering Task Force (IETF) “The concept of Privacy by Design has gotten a lot of attention over the past few years and within the IETF we have tried to investigate how we can consider privacy in the design of protocols and architectural designs in a more systematic way.” — Privacy Considerations for Internet Protocols, Internet Engineering Task Force (IETF), www.ietf.org
  36. 36. Carnegie Mellon University – Privacy By Design •Master's degree program for privacy engineers to be offered by Carnegie Mellon University, School of Computer Science; •The Master of Science in Information Technology-Privacy (MSIT-Privacy) is a 12-month program that began in the fall of 2013; •The program will emphasize the concept of Privacy by Design, in which safeguards are incorporated into the design of systems and products from the very beginning of the development process.
  37. 37. OASIS Technical Committee – Privacy by Design for Software Engineers • Commissioner Cavoukian and Professor Jutla are the Co-Chairs of a new technical committee (TC) of OASIS “PbD-SE (software engineers) TC;” • The purpose of PbD-SE is to provide PbD governance and documentation for software engineers; and • The PbD standards developed will pave the way for software engineers to code for Privacy, by Design.
  38. 38. OASIS and Privacy by Design • 2014 – the OASIS PbD-SE Technical Committee (TC) approved the Privacy by Design Documentation for Software Engineers Version 1.0 as a Committee Specification Draft (CSD), and the Annex Guide to Privacy by Design Documentation for Software Engineers Version 1.0 as a Committee Note Draft (CND); • This vote represents a milestone for the PbD-SE TC, acknowledging the substantial progress that has been made over the last year; • The PbD-SE TC will undertake another review cycle before submitting the CSD and CND to public review.
  39. 39. — Commissioner Cavoukian “Privacy is just as Big as Big Data. The tools exist to systemically protect personal information and bring about the benefits of Big Data. Together we can ensure that Big Data and ‘Big Privacy’ can both be accomplished to enable win-win scenario.”
  40. 40. “There are considerable risks in abandoning de-identification efforts, including the fact that individuals and organizations may simply cease disclosing de- identified information for secondary purposes, even those seen to be in the public interest.” — Commissioner Cavoukian
  41. 41. Privacy and Security by Design
  42. 42. Proposed Approach to Internet of Things Data Security 1. Security by Design – Build security into devices from the outset; 1. Data Minimization – Data which isn’t collected can’t fall into the wrong hands; 1. Notice and choice for unexpected uses – Consumers should be given clear, simple notices of how their data will be used, along with a consent mechanism. Edith Ramirez – US FTC chairwoman CES 2015
  43. 43. Privacy is Good for Business
  44. 44. Consumer Choice and Privacy • There is a strong competitive advantage for businesses to invest in good data privacy and security practices; • “A significant portion of the population is becoming concerned about identity theft, and it is influencing their purchasing decisions.” — Rena Mears, Deloitte & Touche, Survey Reports An Increase in ID Theft and Decrease in Consumer Confidence.
  45. 45. The Bottom Line Privacy should be viewed as a business issue, not a compliance issue Think strategically and transform privacy into a competitive business advantage
  46. 46. Cost of Taking the Reactive Approach to Privacy Breaches Proactive Reactive Class-Action Lawsuits Damage to One’s Brand Loss of Consumer Confidence and Trust
  47. 47. First “Privacy Marketplace” at the International Consumer Electronics Show in Vegas “ Privacy is a hot issue right now. It’s on everyone’s radar … Consumers asking about privacy – that was the big takeaway. These companies in the privacy marketplace, in large part aren’t advocates. They’re entrepreneurs looking to capitalize on market opportunity. They expect a larger privacy marketplace next year and for brands to incorporate “privacy” into their marketing… Anyone, everyone, can understand the need for privacy.” Victor Cocchia CEO, Vysk Speaking at CES: Jan, 2015
  48. 48. Success in the Future will Require Positive-Sum Paradigms 1. Big Data and privacy are not mutually exclusive: • Data is one of the most valuable assets of any organization ; • Privacy is about personally identifiable information; • Consumer demands are creating additional pressures; 2. Proactive privacy drives innovation: • It is entirely possible to achieve privacy in the Big Data era, while also using data analytics to unlock new insights and innovations to move an organization forward; 3. Innovation and privacy: You can have it all: • Organizations will continue to apply data analytics to Big Data in order to advance their strategic goals and better serve their customers. — Commissioner Cavoukian, Using Privacy by Design to achieve Big Data Innovation Without Compromising Privacy
  49. 49. Let’s Banish Zero-Sum!
  50. 50. SmartData: Privacy by Design 2.0 Context is Key
  51. 51. The Next Evolution in Data Protection: “SmartData” Developed by Dr. George Tomko, at the Identity, Privacy and Security Institute, University of Toronto, SmartData represents privacy in the future with greater control of personal information. Intelligent “smart agents” to be introduced into IT systems virtually – thereby creating “SmartData,” – a new approach to Artificial Intelligence, bottom-up, that will contextualize the field of AI .
  52. 52. SmartData: It’s All About User Control It’s All About Context: •Evolving virtual cognitive agents that can act as your proxy to protect your personally identifiable data; Intelligent agents will be evolved to: •Protect and secure your personal information; •Disclose your information only when your personal criteria for release have been met; •Put the user firmly in control – Big Privacy, Radical Control!
  53. 53. Methods of Creating Intelligent Agents • Top-down, rule-based design (traditional AI); • Bottom-up “evolutionary robotics design;” • The combination of a top-down and bottom-up hybrid will yield the most dynamic results.
  54. 54. Southern Ontario Smart Computing Innovation Platform (SOSCIP) “SOSCIP is a groundbreaking research collaboration involving seven leading southern Ontario universities, IBM Canada, and small- and medium-sized enterprises (SMEs) across the province.” Ryerson’s Privacy & Big Data Institute proposal involving SmartData received SOSCIP approval to explore the feasibility of privacy-protective monitoring of health-related outbreaks, using a foundation of intelligent virtual agents as envisioned in SmartData.
  55. 55. A New Approach: Applying Privacy by Design to Surveillance
  56. 56. “As long as the threat of terrorism exists and the global conditions that instantiate those threats continue, effective measures will be needed to counteract terrorism. At the same time, in order for a free and open society to function properly, privacy and civil liberties must be strongly protected.” Privacy-Protective Surveillance
  57. 57. • A new system of surveillance, which enables effective counter-terrorism measures to be pursued – in a privacy-protective manner; • The underlying technology builds on Artificial Intelligence, advances in cryptography involving Homomorphic Encryption, and Probabilistic Graphical Models (involving Bayesian Networks). Introducing PPS: Privacy-Protective Surveillance
  58. 58. Summary of PPS Privacy Protective Surveillance is a positive-sum, “win-win” alternative to current counter-terrorism surveillance systems. It incorporates two primary objectives in its design: 1.An AI system consisting of feature detection that scans the Web and related databases using a “blind-sight” procedure to detect digital evidence relating to potentially suspicious terrorist activity by some, without infringing on the privacy of unrelated individuals; 2.A technological infrastructure to ensure that any personally identifying information (“PII”) on unsuspected individuals is not collected and, in those associated with targeted activity, encrypted PII will only be divulged with judicial authorization (a warrant issued by the court).
  59. 59. Concluding Thoughts • Privacy risks are best managed by proactively embedding the principles of Privacy by Design – prevent the harm from arising – avoid the data breach; • Focus on prevention: It is much easier and far more cost-effective to build in privacy, up-front, rather than after-the-fact; • Abandon zero-sum thinking – embrace doubly-enabling systems: Big Data and Big Privacy; • Get smart – lead with Privacy – by Design, not privacy by chance or, worse, Privacy by Disaster!
  60. 60. Contact Information Ann Cavoukian, Ph.D.Ann Cavoukian, Ph.D. Executive Director Privacy and Big Data Institute Ryerson University 285 Victoria Street Toronto, Ontario M5B 2K3 Phone: (416) 979-5000 ext. 3138 ann.cavoukian@ryerson.ca ann.cavoukian@ ryerson.ca twitter.com/Pri acyBigData

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