This document provides an overview of key concepts regarding the GDPR (General Data Protection Regulation) and best practices for compliance. It discusses the GDPR requirements around privacy, data protection principles, and data subject rights. It also covers topics like data flow mapping, privacy policies, consent, and data minimization. The document emphasizes the importance of understanding what personal data is, knowing what data you collect and how it flows, allowing users to access and delete their data, and fixing privacy policies to be clear and understandable for users.

1. GDPR for Things

2. Hello!
We’re Saskia Videler & Rob Heyman

3. What we’re going to talk about…
GDPR essentials
Data flow mapping
Privacy policies
Best practices

4. Disclaimer
We’re no
lawyers or legal
professionals!

5. Disclaimer
You could call us
“GDPR enthusiasts”

6. Disclaimer
Although that sounds
a bit odd as well…

7. Disclaimer
Anyway…

8. Primer Time

9. General Data Protection
Regulation - from May 25, 2018
• Privacy protection for European citizens
• No more boundless ‘harvesting’ of personal data
• Only data they:
• Need to operate their service for the customer
• Obtained with full consent of the customer
• Data must be stored in Europe and be removed after a few years
• Data subject must be able to edit, delete or transfer their data

10. 6 principles of privacy
• Lawfulness, fairness and transparency.
• Purpose limitations.
• Data minimisation.
• Accuracy.
• Storage limitations.
• Integrity and confidentiality.

11. Rights of the data subject
• Right to information and transparency.
• Right of access and rectification.
• Right to erasure or “right to be forgotten”.
• Right to restriction.
• Right to data portability.

12. Wait, what data?
• Name, age/birthday, gender, address, etc.
• Meta data: location, device(s), frequency, networks,
connections, conversations, Mac-addresses, IP-
addresses, etc

13. GDPR & Things
They are in our homes.
They listen.

14. GDPR & Things
They are on our bodies.
They track.

15. GDPR & Things
They are in our
bedrooms.
They communicate.

16. GDPR for Things
They know stuff about us.
They know us.
Their makers know us.
They can be hacked.

17. Privacy by design

18. How’s our
privacy literacy?
And that of our partners
and coworkers?

19. Privacy Literacy Survey
• It is not just a survey
It is also a FAQ applied to your area of work
• It is a manual
Through application to case, you understand what GDPR means
• It should be a living document
Like a FAQ, it should be updated with expert answers
• Ideal for company or sector wide codes of conduct

20. What is personal information
according to GDPR?
• Voice recording
• Mac address
• IP-address
• Number of visits
• Age ranges
• Professional email address
• Unique identifier
Summary
Personal data is any data that are able to single a (natural) person out of a crowd or a set of data AND that
allow someone to know who that person is. For example, MAC and IP-addresses are considered personal
data because they are unique per connected device and an ISP can look up these addresses and attach a
name or address to them.

21. What data are
we collecting?

22. Think about
• IP-addresses
• MAC-addresses
• Devices
• Usage data
• Name, address, age, gender, relationships, family situation, etc.
• Recordings
• Heatmaps
• Etc.

23. What data do we
absolutely need to
operate our service?

24. How are you
acquiring this data?
?

25. Consent
Contract

26. Ask for personal data
in context

27. Allow them to say
NO

28. What happens with that
data? Who touches,
sees or processes it?
3rd parties?
Are they GDPR compliant?

29. Think about
• The services that you use for:
• User research
• Processing
• Analysis
• Delivery of physical products

30. What could go wrong?
How can we prevent that
from happening?

31. Think about
• Creation and management Data Flow map
• GDPR task force, feat. DPO
• Government (roles, who’s responsible for what?)
• Plan for problems, escalations, emergencies

32. How do we talk about
personal data
to our users?

33. Think about
• Being clear about your goals
• Being clear about data processing
• Use plain, easy to understand, language

35. How can our users
edit or delete
their data?

36. Think about
• Flow of this process, the usability
• The UX
• Actual editing / deletion of data everywhere in the chain
• All data you’ve collected of your users!
That includes meta data, conversations, etc.

37. Ps: check out roeckoe.be for a cool case about this!

38. How can our users
transfer
their data?

39. Think about
• What does the data look like?
• What format is it?
• How are you going to deliver it to them?
• What does the flow look like?

41. Data Flow Mapping

42. Goal and focus
Focus
Accessibility before accuracy
Mapping instead of assessing
Why?
Best starting point for anything data related
Negotiations on data ‘ownership’, thinking of alternatives
Data protection impact assessment requires a mapping

43. Check list
• Three big white papers or (flip-over) sheets +/- A3 size.
• Two markers; one red, one green.
• At least one regular blue pen.
• Big post-its in a striking colour, e.g. yellow.
• Smaller post-its, in two colours, e.g. orange and green.
• An empty ‘Information Asset Inventory’ sheet.
• Camera (phone camera will do).

44. Case 1: Alexa

45. Case 2: Tracking runners on
a running track
• Runners run over a track
with three wifi access
points that hash mac
addresses
• Unique hashes signify the
number of runners
• Returning hashes are
used to measure average
speeds

46. Step one: prepare your
paper
• Draw a horizontal axis representing time
• Draw a vertical axis representing data subject visibility
Datasubject
Time

47. Step two: adding data
points
• Add data points: Data points are places where you can
find personal data in your process
• Name or label the different data points
CV
stack
Rejected
but
interesting
Closet at
HR

48. Step three: connecting the
dots
As data moves through the data cycle, data points are connected by transmissions.
Use a post-it in another colour (orange, for example) for each transmission.
• Draw arrows with a marker between data points to represent the flow or
exchange of data. These flows can be one-way or two-way.
• Add a transmission post-it to each arrow or between two data points. Describe
on it:
• The medium type of the transmission, (e.g. browser; email; dropbox).
• The encryption type of the transmission (e.g. none, end-to-end).
• Whether the transmission concerns all or partial data.
• Go through all data points and transmissions once more. Discuss if any are
missing, and if necessary, use additional post-its to add to the data flow.

49. step three: connecting the
dots
CV
stack
Rejected
but
interesting
Closet at
HR
Mail
none
all
Folder
none
some
Folder
none
some

50. Step four: Control and
access
• Draw circles with a green marker around (groupings of) data point(s),
indicating the controlling organisation for one or more data points. Name
these areas.
• Check if a transmission or data point is part of a larger system or coupled
with other systems. If so, write down the name of this system on a post-it
in a new colour (e.g. green) and find out if other parties have access to the
data. E.g. if Google Docs is used to store or move data, check if Google
has access.
• Which data points or transmissions are most likely to have an extra pair
of eyes watching, and where is a download easily made? In case of a
loose end, someone or something else has access. This can be within or
outside of your organisation. If you recognize a loose end and a risk of
data doubles, write a ‘!’ on the data point, transmission or coupled system
note and add who and what could be copied outside your process.

51. step four: control and
access
CV
folder on
John’s pc
Rejected
but
interesting
Folder
Closet at
HR
Email
none
all
Shared
Printer
none
some
Folder
none
some
Email
provider
Anyone
at our
company
Anyone
with a
key
?
who has
a key?

52. Step five: Identify the gaps
• Having a complete data flow is near impossible
• Add names to missing information and contact these

53. Step six: fill in your data
asset register
• Aim: have a more detailed view of the data
• Handy to discuss data minimalization, storage and
deletion
Data point name -
number
Category
Data value in
database
Personal data
category
Intended
recipients of data
Retention period
or expiery date?
Who controls this
data?
Storage location Storage medium Security measures Purpose Initial source
Consent or legal
permission
Secondary use:
goal compatibility

54. CV
folder on
John’s pc
Rejected
but
interesting
Folder
Closet at
HR
Email
none
all
Shared
Printer
none
some
Folder
none
some
Email
provider
Anyone
at our
company
Anyone
with a
key
?
who has
a key?

55. Questions for after the
mapping
• Do I collect before or after asking consent
• Is all data processed on EU soil?
• Where do I need more access control?
• What if someone asks for the right to access, deletion,
rectification?
• Is there data I do not need at a given point?
• Are there people with access to data they don’t need?

56. Privacy Statements

57. How to fix your
privacy policy

58. Communicating about privacy:
account

59. Communicating about privacy:
in a survey

60. Communicating about privacy:
checkout & payment

61. Communicating about privacy:
privacy statement

62. How to fix your
privacy policy
Clear, unambiguous language.
No jargon or legalese.
Example from Age.co.uk

63. How to fix your
privacy policy
Use icons to communicate
the privacy policy.
Icon set from Aza Raskin at
Mozilla

64. How to fix your
privacy policy

65. What to needs to be in there?
• Data protection officer contact details
• Purposes
• Legal grounds
• Recipients
• Data transfers outside EU
• Storage times of data
• Users have a right to:
• access, port data, rectify, erase, object, withdraw consent
• Complain at data protection authority
• If there is automated decisions making
• If data is needed for a contract, what happens if a user does not provide data

66. We are ___________. You can contact us here ________.
We collect the following data from you _______,________,_______,_________.
We use __________ for _________.
We use __________ for _________.
We use __________ for _________.
This data is being collected by / through ______________________.
Your data is automatically removed from all of our records after _______.
Who has access to your data: (third parties) _______,________,_______,_________.
How secure is your information? (encryption, SSL, disclaimers)
________________________________.
If you want to view, edit or remove your data, you can go here and do so ________________ /
contact us here __________________.
How to fix your
privacy policy

67. More info
The official text of the regulation:
http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32016R0679
The regulation explained by the European Commission: http://
ec.europa.eu/justice/data-protection/index_en.htm
The podcasts we’ve made about GDPR, UX and content:
https://www.efficientlyeffective.fm
Privacy by Design guidelines:
https://www.enisa.europa.eu/topics/data-protection/privacy-by-design?
tab=publications
Remember to check with privacy experts and legal professionals for your
specific situation.

68. Do I know enough?
Do they know enough?
• RTFM! 99 Articles, 88 pages, not exactly a best seller
If you have not read it, can you expect your partners/employees to do
so?
• Thinking you know something
Is sometimes not good enough
• Personal information, what is it?
Forget mapping data flows if you are not sure
• ‘Privacy literacy survey’ to the rescue
Check your literacy level and see what’s needed
• Click here for the current prototype

69. • Ask for consent and data in context.
Be clear, transparent and fair.
• Handle personal data with care.
Allow for viewing, editing and deleting by data subject.
• Know your dataflows!
Risk assessments need to be done regularly.
• Fix your privacy policy.
Make it easy to understand, no legalese allowed!
• GDPR is actually good for UX
It will guide design and content towards transparent, clear
communication and trust.
5 key takeaways

70. Efficiently
Effective Podcast
efficientlyeffective.fm
Thank
you!