This is the slide-deck of the workshop held on April 2, 2019 in Brussels, titled "Towards Value-Centric Big Data". It includes the presentations given by the speakers.

1. ICT 2018
@eSIDES_EU
#valuecentricbigdata

2. Workshop Agenda – Morning session
10.00 Opening and introduction
10.10 Privacy-enhancing technologies, data sharing and ethics - Saila Rinne, Programme Officer Data Policy and
Innovation Unit, European Commission DG Connect
10.20 “You’re monitoring my what…?!” - Balancing privacy against enhanced security outcomes - Duncan Brown,
EMEA Chief Security Strategist - Forcepoint
10.35 e-SIDES: The community paper on RRI guidelines for big data: why we need it and how we can work together
10.45 A “win-win” initiative for value-centric big data and safeguarding “privacy and ethical values” in the PSPS
domain: AEGIS project - Marina Da Bormida, R&I Lawyer and Ethics Expert
11.00 Safe and secure data marketplaces for innovation: SAFE-DEED - Alessandro Bruni, Legal Researcher KU Leuven
11.15 The dangers of tech-determinism: Demystifying AI and reclaiming the future – Barbara Giovanelli, Policy
Officer, Digital Ethics, EPDS European Data Protection Supervisor
11.30 Responsible Research: Analytics when dealing with personal and personalised mobility data: Track&Know -
Prof. Ansar Yasar, University of Hasselt
11.45 Privacy Preserving Technologies - An outlook– Tjerk Timan, Policy Ananlyst, TNO / BDVA
12.00 Networking Lunch

3. 13.00 Knowledge café: towards responsible big data
15.00 Knowledge café reporting to the plenary and open discussion
16.00 End of workshop
Workshop Agenda – Afternoon session

4. e-Sides Ethical and Societal Implications of Data Sciences

5. e-Sides Ethical and Societal Implications of Data Sciences

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8. “You’re monitoring my what…?!”
Balancing privacy against enhanced security outcomes
Duncan Brown
EMEA Chief Security Strategist

9. e-Sides Ethical and Societal Implications of Data Sciences

10. © 2019 Forcepoint | 10

11. Inadvertent
Behaviors
Poorly communicated
policies and user
awareness
Broken Business
Process
Data where it shouldn’t
be, not where it should
be
Rogue
Employee
Leaving the
company, poor
performance review
Criminal Actor
Employees
Corporate espionage,
national espionage,
organized crime
Malware
Infections
Phishing targets,
breaches, BYOD
contamination
Stolen
Credentials
Credential exfiltration,
social engineering,
device control hygiene
MALICIOUS INSIDER COMPROMIZED INSIDERACCIDENTAL INSIDER
TRUSTED USERS PUTTING ORGANISATIONS AT RISK

12. 13%23%64%
MALICIOUS INSIDER COMPROMIZED INSIDERACCIDENTAL INSIDER
TRUSTED USERS PUTTING ORGANISATIONS AT RISK
Source: Ponemon 2018

13. © 2019 Forcepoint | 13
Risk-Adaptive In Action Senior sales rep,
Copenhagen, DK
Monday
January 21 @ 10am
Working while on PTO
+ Additional Context
Thursday
January 24 @ 1pm
Downloads customer records to Excel
+ Additional Context
Wednesday
January 30 @ 9pm
Copy multiple files to staging
area
+ Additional Context
Saturday
February 9 @ 6am
Bulk copy to USB drive
+ Additional Context
Forcepoint Proprietary
Risk Score: 30
Risk-Adaptive Protection
No enforcement action
Risk Score: 50
Risk-Adaptive Protection
Enhanced auditing activated
Evidence available for investigation later
Risk Score: 80
Risk-Adaptive Protection
Copy allowed but file encrypted
No corporate data at risk
Risk Score: 95
Risk-Adaptive Protection
Action is blocked & account is locked
Avoided $10M breach and forensic proof of
the attack is available

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Do privacy concerns outweigh security objectives?
You’re tracking my what???!!!

15. © 2019 Forcepoint | 15
Processing personal data for security is a legitimate interest
“The processing of personal data to the extent strictly necessary and proportionate for
the purposes of ensuring network and information security … constitutes a legitimate
interest of the data controller concerned.”
GDPR Recital 49

16. © 2019 Forcepoint | 16
We already “monitor” our users
Logins
Failed login attempts
Application access
Presence (Skype, WhatsApp, Chatter, Slack, etc)
Cloud apps (sanction, blocked, tolerated, etc)
Web filtering
White- and black-listing
Endpoint process monitoring
Physical card swipes for office access
Join these dots to create an overall risk profile for each user
Have a framework for transparency that protects our users, customers and partners
We should…

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Where should companies strike the balance?
How bad is the Privacy risk
to employees?
How bad is the Security risk
to the firm?

18. Thank you
duncan.brown@forcepoint.com
© 2019 Forcepoint | 18Forcepoint Proprietary

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https://e-sides.eu/collaborative-platform/
RRI principles for privacy-
enhancing technologies
advise and
recommendations on the
best principles of
development of privacy-
preserving
technologies.
Online collaborative space
December 2019

20. Research agenda
20ICT 2018 – Vienna, 5 December 2018
Relevance of the community position paper for the future research
agenda
Building the next Research Agenda
Connect to our
e-SIDES platform
Engage with the
Big Data and
Data Science
Community
Contribute to our
RRI
recommendations
Shape the
priorities for
research
Contribute the
development of
the next
Framework
Programme
Know the topics
of the Work
Programme in
advance

21. This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 732189
• Marina Da Bormida, R&I Lawyer and Ethics
Expert
• April, 2nd 2019, e-SIDES Workshop, Brussels
A “WIN-WIN” INITIATIVE FOR VALUE-CENTRIC BIG DATA AND
SAFEGUARDING “PRIVACY AND ETHICAL VALUES” IN THE PSPS
DOMAIN: AEGIS PROJECT
©dem10/iStock
ADVANCED BIG DATA VALUE CHAIN FOR PUBLIC SAFETY AND PERSONAL SECURITY

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41. This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 825225
Safe and secure data marketplaces for innovation
02.04.2019

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54. The dangers of tech-determinism:
Demystifying AI and reclaiming the future
Barbara Giovanelli
Policy Officer, Digital Ethics
European Data Protection Supervisor

55. Privacy is essential for upholding human dignity in the digital age.

56. AI & GDPR
Lawfulness, fairness and transparency of processing
Purpose limitation
Data minimisation
Storage limitation
Controllership and accountability
Right to be informed
Right not to be subject to a decision based solely on automated processing
...
personal data: “any information relating to an identified or identifiable natural person”

57. “Not everything that is legally compliant and technically feasible is
morally sustainable.”
Giovanni Buttarelli, European Data Protection Supervisor,
at ICDPPC 2018

58. For what purposes, by whom and for the benefit of whom?

59. The myth of an alleged “artificial intelligence”
leads us to ignore the limitations of big data analytics.

60. “The result produced by the machine, using more and more
sophisticated software ... has an apparently objective and
incontrovertible character to which a human decision-maker may attach
too much weight, thus abdicating his own responsibilities.”
European Commission, Amended proposal for Data Protection Directive 1992

61. “AI” fixes social categories rather than developing them.

64. “COMPAS is an algorithm widely used in the US to guide sentencing by predicting the
likelihood of a criminal reoffending. In perhaps the most notorious case of AI prejudice,
in May 2016 the US news organisation ProPublica reported that COMPAS is racially
biased. According to the analysis, the system predicts that black defendants pose a
higher risk of recidivism than they do, and the reverse for white defendants. Equivant,
the company that developed the software, disputes that.
It is hard to discern the truth, or where any bias might come from, because the algorithm
is proprietary and so not open to scrutiny. But in any case, if a study published in
January this year is anything to go by, when it comes to accurately predicting who is
likely to reoffend, it is no better than random, untrained people on the internet.”
New Scientist, April 2018

65. “COMPAS is an algorithm widely used in the US to guide sentencing by predicting the
likelihood of a criminal reoffending. In perhaps the most notorious case of AI prejudice,
in May 2016 the US news organisation ProPublica reported that COMPAS is racially
biased. According to the analysis, the system predicts that black defendants pose a
higher risk of recidivism than they do, and the reverse for white defendants. Equivant,
the company that developed the software, disputes that.
It is hard to discern the truth, or where any bias might come from, because the algorithm
is proprietary and so not open to scrutiny. But in any case, if a study published in
January this year is anything to go by, when it comes to accurately predicting who is
likely to reoffend, it is no better than random, untrained people on the internet.”
New Scientist, April 2018

66. Recognising the limitations of machines
can help us take ethical decisions on their use.

67. “Let's face it, technological determinism is terrible politics.”
Matthew Taylor, December 2018, Wired

68. “When individuals are treated not as persons but as
mere temporary aggregates of data processed at an
industrial scale, they are arguably, not fully respected,
neither in their dignity nor in their humanity.“
EDPS Ethics Advisory Group, 2018

69. “Ethics is not supposed to be easy. Ethics is not there to be convenient.
Ethics is there to challenge your views and notions and convictions on a
daily basis. Ethics is nothing static, it needs to be agile because as a
society we are constantly changing.”
Sandra Wachter, EDPS #DebatingEthics Podcast

72. Under which conditions can ethics committees be influencial?

73. Good AI to be competitive in the global marketplace,
or good AI for the sake of good AI?

74. “Privacy is the legal recognition of the resistance or reticence to
behaviour steered or induced by power. From this point of view, privacy
in a constitutional democratic state represents a legal weapon against the
development of absolute balances of power, again proving privacy's
essential role in such a state.”
Serge Gutwirth & Paul De Hert, 2006

75. Big Data Analytics & GDPR
Lawfulness, fairness and transparency of processing
Purpose limitation
Data minimisation
Storage limitation
Controllership and accountability
Right to be informed
Right not to be subject to a decision based solely on automated processing
...
personal data: “any information relating to an identified or identifiable natural person”

76. What is our vision and how can we use tech to get there, and not let tech drive it?

77. Thank you!
Barbara Giovanelli
Policy Officer, Digital Ethics
barbara.giovanelli@edps.europa.eu
European Data Protection Supervisor

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Towards Value-Centric Big Data
Connect People, Processes and Technology
Knowledge Café Session

105. 105
Station 1: Develop a common ethical and legal framework for responsible innovation by privacy-preserving technologies
Station 2: Define design requirements for big data solutions that lead to a more responsible use of big data
Station 3 How to embed accountability, transparency and responsibility in company processes?
Station 4 What about the business? How to balance business and ethical objectives?
FOUR
4
e-SIDES team
Stations

106. Thank you!
@eSIDES_EU
#valuecentricbigdata
eSIDES_EU
info@e-sides.eu