Presentation of Data Protection

1. Methods and Tools for GDPR Compliance through
Privacy and Data
Protection 4 Engineering
Privacy and Data Protection by
Design
CEA, UPM, Beawre, Trialog
Methods and Tools for Privacy and Data
Protection by Design
2020/03/10

2. Outline
Privacy and Data Protection by Design (PDPbD): context and challenges
Proposed method for PDPbD
Tool support for the method
Personal Data Detector Module
Module for Privacy Model-driven design
Module for Code Validation
Work progress and perspectives
2019/03/10 Privacy and Data Protection by Design PDP4E

3. Context
Design engineers’ ecosystem:
 Several stakeholders and actors
 Variety of needs and objectives
 Solution for conflicting goals/reqs.
Designer’s questions to address:
 Which privacy-aspects introduce
during systems design?
 How identified concerns can be
considered at early design steps?
 How privacy-by-design can be
effectively realized?
2019/03/10 PDP4E
Data
Industry
Developers
Individuals
Policy
makers
Attackers
Wistleblowers
Engineers
Dark/hidden
actors
Image borrowed from https://www.digitalvidya.com/
Privacy and Data Protection by Design
Privacy and Data Protection by Design

4. PDP by Design Method
2019/03/10 PDP4E
Main characteristics:
Combined bottom-up and top-
down approaches:
 From data structures to data and
data-flow (process) models
 Allocation over an architecture
model
 Architecture refinement towards
code
Identification of personal data
Models improved by Privacy-by-
design strategies (ISO 27550)
Validation of properties at code
level
Privacy and Data Protection by Design

5. Tool support for the PDPbD method
2019/03/10 PDP4E
PDPbD Framework
1) Personal Data Detector
- Data structures
- Identified
personal data
- Confidence
scores
Code validation and
verification
- Privacy flaws
- Code improvement
2) Privacy Model-driven designer
3) Module for Code Validation
Target of Validation
- Components
- Pointers to code
- Privacy properties
Privacy and Data Protection by Design

6. Interactions with other PDP4E tools
Tool support for the PDPbD method
2019/03/10 PDP4E
WP5
Risks
Requirements
Assurance
Requirements engineering:
• From GDPR
• As elicited from ProPAn
• Integrating aspects from ISO 29100
Risks analysis:
• Impacted assets
• Countermeasures, PETs
• DFDs ↔ Arch.
Assurance process:
• Reqs. Fulfillment
• Targets of validation
• V&V cases/outcomes
Personal Data Detector
• SQL data
• Scores on SQL data
• Exporting SQL data and
scores
Papyrus Data Models
• Instances of imported SQL
data
• Abstract representation of
imported SQL data
• Extension of UML class
diagrams
Papyrus Process Models
• Processes involving data
• Associations to abstract
representation of data
• Extension of UML Activity
diagrams (DFD)
Papyrus Architecture Models
• Non-automated allocation/mapping to target functional architecture
• Functional architecture : UML Composite Structure diagrams
• Components architecture: UML Composite Structure diagrams
Code Validation
• Requirements/properties
• Frama-C
• SecureFlow
• Extensions for PDP
Privacy and Data Protection by Design

7. PDPbD Framework
2019/03/10 PDP4E
Personal Data Detector
Victor Muntés (Beawre)
Privacy and Data Protection by Design

8. PDPbD Framework
2019/03/10 PDP4E
Privacy Model-driven Designer
Gabriel Pedroza (CEA)
Privacy and Data Protection by Design

9. Personal
data
detector
Privacy and Data Protection
Model-driven Design
PDP4E
- Code validation
and verification
- Code improvement
Risk
Management
Requirem.
Engineering
Systems
Assurance
System (Asset)
models
Evidences
(traceability, V&V…)
Privacy Controls
Requirements
(GDPR, ISO29100)
2019/03/10 Privacy and Data Protection by Design

10. Privacy and Data Protection
Model-driven Design Usage
1)Choose design
strategy to fulfill
goals/requirements
2)Design/enrich
system Process
models
3)Apply strategy
(e.g., inform,
control, enforce,
demonstrate)
1)Choose design
strategy to fulfill
goals/requirements
2)Design/enrich
system data models
3)Apply strategy
(e.g., minimize,
separate, abstract,
hide)
WP5 PDP4E
2019/03/10
Privacy and Data Protection by Design

11. Privacy and Data Protection
Model-driven Design
2019/03/10 PDP4E
Implementation
Excerpt of Art. 7
1) GDPR metamodel
2) Profile: PDPbD Framework DSML
3) Privacy Mechanisms
- GDPR Libraries (patterns)
- PDP Techniques 4) Front-end customization
- GUI, explorer filters
- menus, palettes
Privacy and Data Protection by Design

12. Privacy and Data Protection
Model-driven Design
PDP4E
Data-oriented model
Abstract data
Structured data
User defined data types
Predefined types:
 Table
 DataLink
 OpaqueData
Table Import
Data-oriented strategies:
 Minimize
 Separate
 Abstract
 Hide
2019/03/10 Privacy and Data Protection by Design

13. Privacy and Data Protection
Model-driven Design
PDP4E
Data-oriented strategies (27550 – Privacy Engineering)
2019/03/10 Privacy and Data Protection by Design

14. Privacy and Data Protection
Model-driven Design
Built-in techniques: K-anonymity
Quasi-identifiers
 Models are associated to strategies
 Implemented strategies help to improve models
 Conformity with privacy principles
PDP4E 14
2020/03/10 Privacy and Data Protection by Design

15. Privacy and Data Protection
Model-driven Design
Built-in techniques: K-anonymity
 2-Anonymized table
PDP4E 15
2020/03/10 Privacy and Data Protection by Design

16. Privacy and Data Protection
Model-driven Design
PDP4E
Process-oriented model
DFD implementation:
 External entities
 Data stores
 Processes
 Directed data flows
DFD Refinement
 DFD-L0 to DFD-L1
Data-oriented strategies:
 Inform
 Control
 Enforce
 Demonstrate
Level 0 DFD
2019/03/10
Level 1 DFD
Strategies dialog
Privacy and Data Protection by Design

17. Privacy and Data Protection
Model-driven Design
Implementation of a Data Flow Diagram (DFD)
 Process
 External Entities
 Data Store
 Data Flow Edges
 Input / Output Pins
PDP4E 17
2020/03/10 Privacy and Data Protection by Design

18. Privacy and Data Protection
Model-driven Design
PDP4E
Process-oriented strategies (ISO 27550 – Privacy Engineering)
2019/03/10 Privacy and Data Protection by Design

19. Privacy and Data Protection
Model-driven Design
Built-in technique: Consent Pattern
 The pattern introduces GDPR consent notions
 The pattern is applied on a target DFD model
 Instantiation guidance for the user
PDP4E 19
2020/03/10
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.
Privacy and Data Protection by Design

20. Privacy and Data Protection
Model-driven Design
Built-in technique: Consent Pattern Instantiation
 Detailed view
PDP4E 20
2020/03/10 Privacy and Data Protection by Design

21. Privacy and Data Protection
Model-driven Design
Architecture model
 Components supporting –functional- processes and tasks
 Detailed view of components
 Vulnerabilities
 Privacy measures (PETS)
 Technology
 Subcomponents
 Ports
 Connectors
PDP4E 21
2020/03/10 Privacy and Data Protection by Design

22. Privacy and Data Protection
Model-driven Design
Built-in technique: Process-to-Architecture Allocation
 Generate functional architecture aligned to DFD
 Manually explore allocation: Process/Tasks  Component/subcomponent
 Reference to external artefacts, e.g., code

PDP4E 22
2020/03/10 Privacy and Data Protection by Design

23. PDPbD Framework
2019/03/10 WP5 PDP4E
Module for Code Validation
Julien Signoles (CEA)

24. Current status and perspectives
PDP4E
Deliverables available in (https://pdp4e-project.eu)
Task 5.1:
 D5.4 Specification of the method for Privacy and Data Protection by Design (M14)
Task 5.2:
 D5.1, D5.2 Specification of PDPbD Framework (M14, M18)
Task 5.3:
 D5.6 First release of the PDPbD Framework (M18)
 Perspectives:
Framework validation through Smart Grid Case Study
Consolidation of the PDPbD Framework
Dissemination and exploitation through publications and meetups
2019/03/10 WP5

25. 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?
WP Leader: CEA
gabriel.pedroza@cea.fr