The following presentation was given by Tjerk Timan, Policy Ananlyst from TNO and BDVA, during the e-SIDES workshop "Towards Value-Centric Big Data" held on April 2, 2019 in Brussels.

1. Privacy Preserving
Technologies - an outlook
E-SIDES Conference, Brussels 02 my 2019

2. Data Single Market - regulatory challenges
Data
Processing data Applying data-driven innovations
personal non-personal AI and ML, cloud computing
data
protection
for EU citizens
free flow of data
and geo-blocking
IP and copyright,
data-, and AI marketplaces,
cybersecurity & access
novel business propositions,
value creation, lean policy
algorithmic transparency,
inclusiveness
for the BDVA - support via ‘how to’s’ and best practices (if any)
on most pressing topics (GDRP implementation for startups for example)
BDVe context

3. What?
1. an overview of technologies to
inform policymakers
2. an aligning of research agendas
3. a discussion on future challenges

4. Why?
1. To anVcipate new challenges for human
values resulVng from big data collecVon
and analyVcs (it is about more than privacy)
2. To explore technical soluVons to safeguard
those rights or to enforce them

6. What is new?
1. From big data to data4AI
2. Geographical scope (where is big data?)
3. Conceptual scope (what is (personal) data?)
4. Socio-politcal scope (why is it relevant?)

8. technology to fix trust
broken by technology

9. Sticky policies
https://www.hpl.hp.com/techreports/2003/HPL-2003-49.pdf
What does it do?
codifies policies and sends
it along with a dataset
What does it solve?
Enforcement of regulation
New Problem?
Needs a trusted party -
needs updates

10. Self-Sovereign Identity
What does it do? Turns data control back to user
What does it solve? Power balance and lack of data control
New Problem? Cognitive load - assumption that control is
positive

11. Multi-Party Computation
What does it do? makes
possible to analyse big data
without seeing it
What does it solve?
Anonimisation and data
sharing - third party problem
New Problem? Performance -
only works on aggregatate
level

12. Explainaible AI
What does it do? Explains decisions made by ML
What does it solve? Inherent intransparencey of DL/ML
New Problem? Recursiveness: How transparent are XAI
outcomes?

13. An argument line: privacy > digitisation > data
protection > regulation > technical solutions > new
challenges
Solutions on data: access, control, transmission,
sharing, interception… about establishing trust
between 2 (or more) partie
However: new solutions also create new challenges

14. https://www.theguardian.com/commentisfree/2019/apr/01/government-regulate-facebook-mark-zuckerberg-social-media
PPTs aid self-regulation,
but is that enough?
How can PPT uptake be
stimulated, or
enforced?
When/ how to
responsibility end-
users?

15. PPT maturity
Societal
uptake
PPT
technical factors:
- experVse needed to implement
- effect or performance
- compaVbility
- updatability
- standardised vs customised
societal factors
- role of privacy in society
- plaaorm dominance
- understanding of data
- use/role of big data and AI
- level of trust in digital tech

16. Some questions for the afternoon
1. What is data4AI that big data is not?
2. What can we say about privacy&AI?
3. Should we still talk about privacy?
4. What are the limits of techno regulation - do
we have choice?