Discussion on Confidentiality in data science: “Data science that ensures confidentiality – How to answer questions without revealing secrets?” (session leader prof.dr. Ronald Leenes) To future-proof responsible data science methods, foundational research is needed, and, given the complementarity of TU/e and TiU in JADS, there are great opportunities to collaborate on this theme. This was reflected by the JADS Workshop on Responsible Data Science

1. JADS-ws on Responsible Data Science
confidentiality
prof.dr. Ronald Leenes
Understanding Society

4. data determinism
identity theft
data protection

5. in a moral community people
do the right things for the
right reasons
(not because it is the law)*
*but a stick might help

6. “confidentiality”

7. 95/46/ECData protection Directive

8. GDPRCOM(2012) 11 final 25-01-2012
General Data Protection Regulation

9. onfidentialitypersonal data
fairness
autonomy
transparency
&
accountability
purpose limitation
data minimisation
security

10. the GDPR introduces/reinforces some
serious challenges for data scientists
claim
purpose limitation
data minimisation
transparency
accountability
privacy by design

11. data security will become an even more
challenging topic
claim
cybercrime
data breach notification
secure & process

12. anonymization is dead/dying and Data
Science slams the last nails in the coffin
claim
reidentification
profiles applied
all data are personal data*
*right, Nadya

13. 1. (how) can we reconcile Data Science with
‘GDPR’?
2. can data security be reconciled with
processing?
3. do we have to stop worrying and learn to
love processing personal data?
questions

14. time
r.e.leenes@uvt.nl