Big Data: Little Ethics?
Legal Aspects of Big Data
HWZ-Darden Conference
4 June 2015
Roland Mathys, LL.M. (LSE)
Attorney at law / Partner

Agenda
> Introduction
> Fundamentals
> Principles of data processing
> Proportionality
> Purpose limitation
> Transparency
> Accuracy
> Failure of traditional approaches
> Anonymization
> Consent
> Emergence of new approaches
> Conclusion
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#UBERDATA – “Rides of Glory” (RoG)
> A RoGer is anyone who takes a ride between 10pm and 4am, and then takes a second ride from within 1/10th of a mile of the previous night’s drop-off point 4-6 hours later (enough for a quick night’s sleep).
> Big Data Analysis
SF
Legal Aspects of Big Data
NY Boston
Source: uber.com
4 June 2015
Chicago DC
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“Pizza Palace”
Legal Aspects of Big Data
Source: https://www.aclu.org/ordering-pizza
4 June 2015 Page 4/15

Fundamentals of Data Protection
> Data protection
> Protection of a person’s privacy when his or her data is processed
> Privacy: Human, fundamental and constitutional right in many jurisdictions
> Personal data
> Information related to an identified or identifiable person
> Person: Natural person and (in some jurisdictions) legal entities
> Specific personal data
> Sensitive personal data: E.g. related to race, origin, health, crimes
> Personality profiles: Data collection permitting assessment of personality
> Processing
> Any operation with personal data, irrespective of the means applied and the procedure, and in particular the collection, storage, use, revision, disclosure, archiving or destruction of data
> Virtually everything!
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Principles of Data Processing (1)
Lawfulness
Accuracy Good Faith
Legal Aspects of Big Data
Transparency
Purpose
Limitation
4 June 2015
Proportionality
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Principles of Data Processing (2)
> Proportionality
> Which data, how much data?
> Whom to share with?
> How long to keep it?
> Purpose limitation: Processing purposes have to be
> indicated at time of collection;
> evident from the circumstances; or
> based on statutory provision.
> Transparency
> If personal data is shared with third parties?
> If personal data is anonymized and eventually re-identified?
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Principles of Data Processing (3)
> Accuracy
> Big data analysis based on correlations, not causalities
> Findings may be correct in many, but not in all cases:
Joe Pyne, Moderator:
“You have long hair, so you are a girl?”
Frank Zappa, Musician:
“You have a wooden leg, so you are a table?”
Source: tvdays.com
Source: ibigdan.livejournal.com
Imagine you have no own car, you are regularly working during the night shift (from 11 pm to 5 am), and, for reasons unknown to you
(and to your wife!), start to receive ads for escort services …
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Failure of Traditional Approaches
Legal Aspects of Big Data
Consent
Right to
Information,
Access,
Correction
Anonymization
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“The Legend of Anonymization”
> What information do you need to identify 89% of U.S. citizens?
> Birth date
> Sex
> Postal code
> Anonymized Traffic Metering
> Based on GPS data, collected from mobile devices
> Swiss village with 3’000 residents
> Analysis
Average Speed
~
5 km/h
20 - 30 km/h
~
50 km/h
> 50 km/h
10 km/h
Sample Size Means of Transportation
Many Pedestrians
Many Cyclists
Many Motorists
Some “Speeders”
1 (!) ???
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“The Myth of Consent”
> For consent to be valid, it must be given
> informed;
> freely;
> and in some cases express.
> Informed consent
> Do you know what you are consenting to?
> Transparency?
> Free consent
> “Take it or leave it!”
> “Take it or pay for it!”
> Alternatives on the market?
Source: facebook.com
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So What?
"
"
Scott McNealy, Chairman Sun Microsystems (1999!)
"
"
Marc Zuckerberg, CEO Facebook (CNBC on 3 December 2009)
"
"
Eric Schmidt, CEO Google (The Guardian of 11 January 2010)
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Emergence of New Approaches
Data
Ownership
(Private,
Public, Open)
"Profiling"
Restrictions
“Certification”
(PIA, Privacy by Design,
Privacy by
Default)
New Balance between
Benefits of Big
Data and Data
Protection
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Conclusion
> Data protection is still an issue in the big data era – or even more!
> Big data falls foul of many core principles of data protection.
> Traditional approaches are not suitable to address big data issues.
> New and innovative approaches will be paramount for effective data protection.
> Data protection legislation needs evolution, but no revolution.
> Legislative action alone won’t solve the issue!
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Thank you for your attention!
Roland Mathys, LL.M. (LSE)
Attorney at law / Partner roland.mathys@swlegal.ch
Schellenberg Wittmer Ltd / Attorneys at Law
Löwenstrasse 19 / P.O. Box 1876 / 8021 Zurich / Switzerland
T +41 44 215 5252 / F +41 44 215 5200 www.swlegal.ch
ZURICH / GENEVA / SINGAPORE
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