The following presentation was given by Daniel Bachlechner (Fraunhofer ISI & e-SIDES) during the BDVe webinar "New challenges for data regulation beyond the GDPR: how to assess risks and develop tools in an era of AI?" held on January 31, 2020.

1. Why are privacy-preserving
technologies not used more widely?
Daniel Bachlechner, Fraunhofer
BDVe Webinar
31 January 2020
Source:https://www.ethicalsocietymr.org/upcoming-events.html

2. Improve the dialogue
between stakeholders
and increase the
confidence of citizens
in data technologies
and use
e-Sides Ethical and Societal Implications of Data Sciences 2
Objectives and methods
▪ Investigation of related projects
through joint workshops,
interviews and website analyses
▪ Collection of insight from
renowned experts with
different backgrounds through
workshops and interviews
▪ Review of more than 200
articles including academic
papers and study reports
▪ Interaction with a diverse set of
stakeholders by means of a
collaborative platform
Key objectives Main methods
Reach a common vision
for an ethically sound
approach to data use
and facilitate
responsible research
and innovation

3. e-Sides Ethical and Societal Implications of Data Sciences 3
Phases and focus
36-months project
Assess existing
technologies
Identify
implementation
barriers
Make recom-
mendations
Assess solutions
under
development Identify design
requirements
Identify ethical
and societal
issues
Identify existing
technologies
Identify existing
technologies
Conduct a gap
analysis
Conduct a gap
analysis
Assess existing
technologies

4. 4
Existing technologies
Anonymisation
Encryption Accountability
Deletion
Policy enforcement
MPC
Sanitisation
Transparency
Access control
User control
Access & portability
Data provenance
Resources: D3.1, white paper
Technology classes
Anonymisation
Encryption Accountability
Deletion
Policy enforcement
MPC
Sanitisation
Transparency
Access control
User control
Access & portability
Data provenance

5. ▪ Limited integration into today’s
big data solutions
▪ Low demand for privacy-
preserving big data solutions
▪ Considerable regional differences
regarding perception and use
▪ Combination with non-technical
measures needed
▪ Unclear responsibilities for
protecting privacy
5
Existing technologies
Resources: D3.2, white paper, WISP publication
Effectiveness and challenges Perception and use
▪ The set of technology classes is
comprehensive
▪ Classes of technologies need to
be combined to be effective
▪ Technologies pursue different
aims
▪ A multidimensional measure is
required
▪ There is tension between
objectives
▪ Limited integration into today’s
big data solutions
▪ Low demand for privacy-
preserving big data solutions

6. Relevant societal and economic aspects
6
Limited integration
Costs and benefits
Business models
Public attention
Economic value
Cultural fit
Skill level
Resources: D4.1, white paper
Costs and benefits ▪ Preserving privacy leads to additional costs but
there is only little information about the
amount of costs
▪ User inconvenience, for example, has been
described as a relevant cost factor
▪ There is no evidence that privacy-preserving
solutions lead to increased sales or justify
higher prices
▪ The use of privacy-preserving technologies must
make economic sense

7. Relevant societal and economic aspects
7
Limited integration
Costs and benefits
Business models
Public attention
Economic value
Cultural fit
Skill level
Resources: D4.1, white paper
▪ Privacy preservation may be in conflict with
business models
▪ Profits made are often not shared through
innovative business models
▪ Trade-off between privacy protection and the
utility of data
▪ Fear of limitations in flexibility and the ability to
innovate
Business models

8. Relevant societal and economic aspects
8
Limited integration
Costs and benefits
Business models
Public attention
Economic value
Cultural fit
Skill level
Resources: D4.1, white paper
Public attention
▪ Privacy protection is not yet a standard
business practice
▪ Actors tend to take extreme positions
regarding privacy preservation
▪ Potential to allow for competitive
differentiation (e.g., Apple)
▪ Limited transparency with respect to algorithms
and data provenance

9. Relevant societal and economic aspects
9
Limited integration
Costs and benefits
Business models
Public attention
Economic value
Cultural fit
Skill level
Resources: D4.1, white paper
Economic value
▪ No shortage of economic literature attempting
to quantify the value of data
▪ Privacy concerns and expectations are context-
dependent and difficult to predict
▪ Privacy-unfriendly companies tend to obtain the
greater market share
▪ The value of privacy seems to depend on the
social class to which an individual belongs

10. Relevant societal and economic aspects
10
Limited integration
Costs and benefits
Business models
Public attention
Economic value
Cultural fit
Skill level
Resources: D4.1, white paper
Cultural fit
▪ Privacy preferences and practices vary
among nations and regions
▪ Broad spectrum of views regarding impacts:
from deindividualization to personalization
▪ Less involvement in data management tasks
preferred
▪ Extent to which unauthorized secondary use
raises concerns differs

11. Relevant societal and economic aspects
11
Limited integration
Costs and benefits
Business models
Public attention
Economic value
Cultural fit
Skill level
Resources: D4.1, white paper
Skill level
▪ Often critical people do not know what
questions to ask
▪ The data becomes more and more important
a new mind-set is required
▪ Integrating and using privacy-preserving
technologies requires specific skills

12. Source:https://leadg2.thecenterforsalesstrategy.com/book
12
Embed security and
privacy features
Connect people,
processes and
technology
Take preventive
measures
Comply with laws and
corporate policies
Resources: D4.2
Connect people,
processes and
technology
Design requirements for data-driven solutions
Embed security and
privacy features
Take preventive
measures
Comply with laws and
corporate policies
Going forward

13. Source:https://www.marblespr.co.uk/news/walk-will-give-next-big-idea-legs-needs/attachment/...
13
Going forward
Developers and
operators
of data-driven
solutions
Policy makers
dealing with
relevant issues
Developers of
privacy-preserving
technologies
Civil society
(organisations)
Resources: D5.2
Policy makers
dealing with
relevant issues
Making responsible data driven solutions a reality
Developers and
operators
of data-driven
solutions
Developers of
privacy-preserving
technologies
Civil society
(organisations)

14. Thank you!
@eSIDES_EU
#privacyinbigdata
eSIDES_EU
info@e-sides.eu
https://e-sides.eu/