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Data Governance: Addressing the Big Data Challenge
Data Governance: Addressing the Big Data Challenge
The Decade of Privacy by Design
The Decade of Privacy by Design
Adoption of Privacy by Design as an International Standard
Adoption of Privacy by Design as an International Standard
Privacy by Design: Proactive in 37 Languages
Privacy by Design: Proactive in 37 Languages
Abandon Zero-Sum (Win/Lose) Paradigms
Abandon Zero-Sum (Win/Lose) Paradigms
replace vs
replace vs
Privacy by Design: The 7 Foundational Principles
Privacy by Design: The 7 Foundational Principles
Operationalizing Privacy by Design
Operationalizing Privacy by Design
Cost of Taking the Reactive Approach to Privacy Breaches
Cost of Taking the Reactive Approach to Privacy Breaches
Letter from JIPDEC  May 28, 2014
Letter from JIPDEC May 28, 2014
Big Data
Big Data
Big Data
Big Data
Big Data Technology is Not Foolproof
Big Data Technology is Not Foolproof
Big Data: More Than Just Input
Big Data: More Than Just Input
Big Data is moving from its inflated expectations phase to a trough of
Big Data is moving from its inflated expectations phase to a trough of
Context is Key
Context is Key
Privacy Breeds Innovation: It Does NOT Stifle It
Privacy Breeds Innovation: It Does NOT Stifle It
Privacy is just as Big as Big Data
Privacy is just as Big as Big Data
There are considerable risks in abandoning de-identification efforts,
There are considerable risks in abandoning de-identification efforts,
Internet of Things (IoT)
Internet of Things (IoT)
Internet of Things: Three Broad Categories
Internet of Things: Three Broad Categories
Internet of Things: Privacy Risks
Internet of Things: Privacy Risks
EU Article 29 Working Party
EU Article 29 Working Party
Privacy Commissioners Declaration
Privacy Commissioners Declaration
Proposed Approach to Internet of Things: Data Security
Proposed Approach to Internet of Things: Data Security
There is an Essential Need to Embed Privacy into IoT and Mobile
There is an Essential Need to Embed Privacy into IoT and Mobile
Privacy is Good for Business
Privacy is Good for Business
Privacy should be viewed as a business issue, not a compliance issue
Privacy should be viewed as a business issue, not a compliance issue
The Argument that Privacy Stifles Big Data Innovation Reflects a Dated
The Argument that Privacy Stifles Big Data Innovation Reflects a Dated
Concluding Thoughts
Concluding Thoughts
Contact Information
Contact Information

: Data Governance: Addressing the Big Data Challenge. : Jason Papadimos. : Data Governance: Addressing the Big Data Challenge.ppt. zip-: 1146 .

Data Governance: Addressing the Big Data Challenge

Data Governance: Addressing the Big Data Challenge.ppt
1 Data Governance: Addressing the Big Data Challenge

Data Governance: Addressing the Big Data Challenge

Ann Cavoukian, Ph.D. Executive Director Privacy and Big Data Institute Ryerson University

Information Technology Law Spring Forum Toronto, Ontario May 4, 2015

2 The Decade of Privacy by Design

The Decade of Privacy by Design

3 Adoption of Privacy by Design as an International Standard

Adoption of Privacy by Design as an International Standard

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

4 Privacy by Design: Proactive in 37 Languages

Privacy by Design: Proactive in 37 Languages

English French German Spanish Italian Czech Dutch Estonian Hebrew Hindi Chinese 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

5 Abandon Zero-Sum (Win/Lose) Paradigms

Abandon Zero-Sum (Win/Lose) Paradigms

6 replace vs

replace vs

with and

Positive-Sum Model: The Power of And

Change the paradigm from a zero-sum to a positive-sum model: Create a win-win scenario, not an either/or (vs.) involving unnecessary trade-offs and false dichotomies

7 Privacy by Design: The 7 Foundational Principles

Privacy by Design: The 7 Foundational Principles

Proactive not Reactive: Preventative, not Remedial; Privacy as the Default setting; Privacy Embedded into Design; Full Functionality: Positive-Sum, not Zero-Sum; End-to-End Security: Full Lifecycle Protection; Visibility and Transparency: Keep it Open; Respect for User Privacy: Keep it User-Centric.

www.ipc.on.ca/images/Resources/7foundationalprinciples.pdf

8 Operationalizing Privacy by Design

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.

9 Cost of Taking the Reactive Approach to Privacy Breaches

Cost of Taking the Reactive Approach to Privacy Breaches

Reactive

Proactive

10 Letter from JIPDEC  May 28, 2014

Letter from JIPDEC May 28, 2014

Privacy by Design is considered one of the most important concepts by members of the Japanese Information Processing Development Center We have heard from Japans 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

11 Big Data

Big Data

12 Big Data

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.

13 Big Data Technology is Not Foolproof

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.

14 Big Data: More Than Just Input

Big Data: More Than Just Input

In the afterglow of Big Datas buzz, many organizations are finding that successful programs require much more than simply plugging data into a program.

Evanta Leadership Network, May 29, 2014.

15 Big Data is moving from its inflated expectations phase to a trough of

Big Data is moving from its inflated expectations phase to a trough of

disillusionment.

Gartner Hype Cycle, April 2014

16 Context is Key

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.

17 Privacy Breeds Innovation: It Does NOT Stifle It

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!

18 Privacy is just as Big as Big Data

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.

Commissioner Cavoukian

19 There are considerable risks in abandoning de-identification efforts,

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

20 Internet of Things (IoT)

Internet of Things (IoT)

21 Internet of Things: Three Broad Categories

Internet of Things: Three Broad Categories

1) Wearable Computing: Everyday objects i.e. Google glass, Apple watch 2) Quantified Self: Record information about ones habits, lifestyle and activities i.e. Fitness and sleep trackers 3) Home Automation: Computer controlled thermostats, light bulbs etc.

22 Internet of Things: Privacy Risks

Internet of Things: Privacy Risks

Third party monitoring removes control of ones information from the individual involved; The nature of the devices may make it more difficult to obtain consent before data collection begins; Specific instances of data collection may not seem important on their own, but when aggregated, they can create a comprehensive picture of a person that may be extremely harmful to the individuals involved, especially in the hands of unauthorized third parties.

23 EU Article 29 Working Party

EU Article 29 Working Party

Recommendations on the Internet of Things: Make privacy the default setting follow Privacy by Design, delete all raw data after processing; Respect a users self-determination over their own data, and seek consent in a user-friendly way; Be transparent about how a users data is being used; When sensors are continuously collecting ones personal data, remind users of this surveillance activity; Ensure that data published to social platforms remain private, by default; Users should not be penalized for failing to consent; Data should be De-Identified, except when necessary.

24 Privacy Commissioners Declaration

Privacy Commissioners Declaration

36th Intl Conference of Data Protection and Privacy Commissioners The value of Internet of Things (IoT) is not only in the devices, but in the services that arise from their use; Connectivity is ubiquitous: it is the joint responsibility of all actors to ensure trust in connected systems : Transparency is Key; Protection should begin from the moment data that is collected; Privacy by Design should be the key selling point of innovative technologies Strong, active and constructive debate is necessary to overcome the huge challenges presented by the developers of IoT.

-September, 2014 Mauritius

25 Proposed Approach to Internet of Things: Data Security

Proposed Approach to Internet of Things: Data Security

Security by Design Build security into devices from the outset; Data Minimization Data which isnt collected cant fall into the wrong hands; 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

26 There is an Essential Need to Embed Privacy into IoT and Mobile

There is an Essential Need to Embed Privacy into IoT and Mobile

Devices, by Design

27 Privacy is Good for Business

Privacy is Good for Business

28 Privacy should be viewed as a business issue, not a compliance issue

Privacy should be viewed as a business issue, not a compliance issue

The Bottom Line

Think strategically and transform privacy into a competitive business advantage

29 The Argument that Privacy Stifles Big Data Innovation Reflects a Dated

The Argument that Privacy Stifles Big Data Innovation Reflects a Dated

Zero-Sum Mindset

Big Data and privacy are not mutually exclusive: Data is one of the most valuable assets of any organization ; Privacy is about personal information; Consumer demands are creating additional pressures; 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; 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

30 Concluding Thoughts

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: Yes, we can; Get smart lead with Privacy by Design, not privacy by chance or, worse, Privacy by Disaster!

31 Contact Information

Contact Information

Ann Cavoukian, Ph.D. Executive Director Privacy & Big Data Institute Ryerson University 285 Victoria Street Toronto, Ontario M5B 2K3 Phone: (416) 979-5000 x 3138 ann.cavoukian@ryerson.ca

Data Governance: Addressing the Big Data Challenge
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