Privacy Data Protection by Design Framework.

1. Methods and Tools for GDPR Compliance through
Privacy and Data
Protection 4 Engineering
PDPbD Framework
Improving Privacy and Data
Protection by Design
CEA
2020/03/10

2. Case Study: Smart Grid system
Overall description of the target Smart Grid system
2020/03/10 CEA PDP4E 2

3. Smart Grid functions and data flows
PDP4E 3
2020/03/10 CEA

4. Smart Grid reference architecture
PDP4E 4
2020/03/10 CEA

5. Demoing PDPbDesign Framework
PDP4E 5
2. Develop a data-oriented model
3. Built-in privacy techniques for data-oriented models
4. Develop a process-oriented model
5. Built-in privacy techniques for process-oriented models
Continue the development cycle
2020/03/10 CEA
DesignOK
DesignNotOK
1. Select GDPR requirements to be satisfied

6. 1. Select GDPR Requirements to
be satisfied
PDP4E 6
2020/03/10 CEA

7. 1. Select GDPR Requirements to be
satisfied
GDPR-Req1.1:
Processing "Store Measurementinfo" shall be lawful if the data subject "Customer" has
given consent "ConsentForStoreMeasurementInfo" to the processing of his or her
personal data "MeasurementInfo " for one or more specific purposes R-02
GDPR-Req10:
Personal Data "MeasurementInfo" shall be adequate, relevant and limited to what is
necessary in relation to the purpose "R-02" for which they are processed
PDP4E 7
2020/03/10 CEA

8. 2. Develop a data-oriented
model
PDP4E 8
2020/03/10 CEA

9. 2. Develop a data-oriented model
Data-oriented model features:
Different data types:
 Generic Data
 Composite Data: for generic Data
Decomposition
 Table: a tag for imported Tables
 Data Link: reference external sources
 Opaque Data: data non-interpretable, e.g.,
cyphered text
Tool support for modelling:
 Automatic decomposition  go to Tool
PDP4E 9
2020/03/10 CEA

10. 2. Develop a data-oriented model
Framework support for modelling:
Import Data Base (Tables)
Model elements search, selection, edition
  go to Tool
PDP4E 10
2020/03/10 CEA

11. 2. Develop a data-oriented model
Framework support for modelling:
Import Data Base (Tables)
Model elements search, selection, edition
  go to Tool
PDP4E 11
2020/03/10 CEA

12. 3. Built-in privacy techniques for
data-oriented models
PDP4E 12
2020/03/10 CEA

13. 3. Built-in privacy techniques for data-
oriented models
Data-oriented strategies (27550 – Privacy Engineering)

PDP4E 13
2020/03/10 CEA

14. 3. Built-in privacy techniques for data-
oriented models
K-anonymity:
Quasi-identifiers
PDP4E 14
2020/03/10 CEA

15. 3. Built-in privacy techniques for data-
oriented models
K-anonymity:
Example
PDP4E 15
2020/03/10 CEA

16. 3. Built-in privacy techniques for data-
oriented models
Built-in strategy for K-anonymity:
Support to verify the property  go to Tool
PDP4E 16
2020/03/10 CEA

17. 3. Built-in privacy techniques for data-
oriented models
Built-in strategy for K-anonymity:
Support to verify the property  go to Tool
PDP4E 17
2020/03/10 CEA

18. 3. Built-in privacy techniques for data-
oriented models
Built-in strategy for K-anonymity:
GDPR-Req10:
Personal Data "MeasurementInfo" shall be adequate, relevant and limited to what is
necessary in relation to the purpose "R-02" for which they are processed
Links can be settled to show Requirement coverage.
PDP4E 18
2020/03/10 CEA

19. 4. Develop a process-oriented
model
PDP4E 19
2020/03/10 CEA

20. 4. Develop a process-oriented model
Implementation of a Data Flow Diagram (DFD)
 Process
 External Entities
 Data Store
 Data Flow Edges
 Input / Output Pins
PDP4E 20
2020/03/10 CEA

21. 4. Develop a process-oriented model
The DFD implementation is a Domain Specific Modelling Language (DSML)
 Based upon Papyrus UML Activities
 A profile is developed:
 Extension of UML Activity
 Specialization to introduce Domain Knowledge
 Basis for Model-driven design
Support provided for modelling:
 Transformation Requirement-to-DFD
 Reuse of data-oriented model elements
 Automatic application of profile types  go to Tool
PDP4E 21
2020/03/10 CEA

22. 4. Develop a process-oriented model
Support for Process refinement
 Detailed model: the tasks of a process
 A Behaviour associated to each Process
 Consistency between refinements  Go to Tool
PDP4E 22
2020/03/10 CEA

23. 5. Built-in privacy techniques for
process-oriented models
PDP4E 23
2020/03/10 CEA

24. 5. Built-in privacy techniques for
process-oriented models
Process-oriented strategies (27550 – Privacy Engineering)

PDP4E 24
2020/03/10 CEA

25. 5. Built-in privacy techniques for
process-oriented models
Conditions for Consent (GDPR):
 Where processing is based on consent, the controller shall be able to demonstrate that the data
subject has consented to processing of his or her personal data.
If the data subject’s consent is given in the context of a written declaration which also concerns other
matters, the request for consent shall be presented in a manner which is clearly distinguishable from
the other matters, in an intelligible and easily accessible form, using clear and plain language. Any part
of such a declaration which constitutes an infringement of this Regulation shall not be binding.
The data subject shall have the right to withdraw his or her consent at any time. The withdrawal of
consent shall not affect the lawfulness of processing based on consent before its withdrawal. 3Prior to
giving consent, the data subject shall be informed thereof. It shall be as easy to withdraw as to give
consent.
When assessing whether consent is freely given, utmost account shall be taken of whether, inter alia,
the performance of a contract, including the provision of a service, is conditional on consent to the
processing of personal data that is not necessary for the performance of that contract.

PDP4E 25
2020/03/10 CEA

26. 5. Built-in privacy techniques for
process-oriented models
DSML integrating GDPR fundamental notions and relations
 The PDPbD Framework is enriched with
PDP4E 26
2020/03/10 CEA

27. 5. Built-in privacy techniques for
process-oriented models
Built-in technique: Consent Pattern Instantiation
 The pattern introduces GDPR consent notions
 The pattern is applied on a target DFD model
 The user is guided through the instantiation
 The model is accordingly completed
  go to Tool
PDP4E 27
2020/03/10 CEA

28. 5. Built-in privacy techniques for
process-oriented models
Built-in technique: Consent Pattern Instantiation
 The instantiated pattern provides a view of stakeholders:
 Decisive role in GDPR compliance
 Automatic model completion
 Model elements editable
  go to Tool
PDP4E 28
2020/03/10 CEA

29. 5. Built-in privacy techniques for
process-oriented models
Built-in technique: Consent Pattern Instantiation
GDPR-Req1.1:
Processing "Store Measurementinfo" shall be lawful if the data subject "Customer" has
given consent "ConsentForStoreMeasurementInfo" to the processing of his or her personal
data "MeasurementInfo " for one or more specific purposes R-02
Links can be settled to show Requirement coverage.
PDP4E 29
2020/03/10 CEA

30. Methods and Tools for GDPR Compliance through
Privacy and Data
Protection 4 Engineering
For more information, visit:
www.pdp4e-project.org
Thank you for your attention
Questions?
Contributions:
gabriel.pedroza@cea.fr
Patrick.Tessier@cea.fr