CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
Wp6 public
1. Methods and Tools for GDPR Compliance through
Privacy and Data
Protection 4 Engineering
Alejandra Ruiz, Jabier Martinez, Javier Puelles, Izaskun Santamaria (Tecnalia)
Yod Samuel Martin, Jacobo Quintáns, Juan Carlos Yelmo (UPM)
Guillaume Mockly, Estibaliz Arzoz Fernández, Amelie Gyrard, Antonio Kung (Trialog)
Assurance Tool and Method (WP6)
This project has received funding from the European Union's Horizon 2020
programme under Grant Agreement No 787034.
11/06/2021
3. Introduction & Objectives
WP6 Methods and tools for assurance
Participants: Tecnalia (leader), Trialog, UPM
Duration: M10 – M33
Objectives:
A method to demonstrate compliance with privacy and data protection regulation, including the systematic capture and
recording of evidences, their association to requirements and artefacts, their traceability to the GDPR and other
regulations and standards, and the argumentation of compliance derived from the evidences.
A standard metamodel to represent the relevant terms to GDPR compliance, including relevant processes, roles...etc.
A computer-readable knowledge base which contains models of the normative framework that represent GDPR and other
regulation (e.g. WP29 guidance) as well as other data protection standards and mappings between one another, and
assurance patterns.
A software tool, developed by extending OpenCert, which implements the functions needed to support the method and
which hosts the knowledge base.
Output:
Specification of the method and tool: D6.1, D6.2 and D6.3
Method releases: D6.4 and D6.5
Tool releases: D6.6 and D6.7
Knowledge base: D6.8
WP6
3
4. Results: Outline
Demonstrated feasibility of
using state-of-the-art assurance principles for privacy engineering
modelling privacy regulations as reference framework models
handling ecosystems of privacy reference frameworks
providing reusable privacy assurance patterns
Tool-supported
WP6
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5. Results
Using state-of-the-art assurance principles for privacy engineering
A privacy assurance case, is a structured argument supported by a body of evidence, which
provides a convincing and valid justification that a system meets its assurance requirements,
for a given application in a given operating environment *
WP6
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*Adapted to privacy from the safety world:
Denney et al.
“Hierarchical Safety Cases.” In NASA Formal Methods, 2013
6. Results
Using state-of-the-art assurance principles for privacy engineering
WP6
6
GPDR Art. 5: Principles relating to processing of personal data
Paragraph 1
Lawfulness, fairness and transparency
Purpose limitation
Data minimisation
Accuracy
Storage limitation
Integrity and confidentiality
7. Supplier Chain
Component
Release
Module Assurance
Case Development
(Independent)
Safety Assessment
Safety
Assessment
Certification
Liaison
Product Engineering
“Project”
Quality
Management
Implementation
Validation &
Verification
Design
Results
Using state-of-the-art assurance principles for privacy engineering
WP6
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Assurance
“Project”
Assurance Case
Development
Evidence
Management Assurance Process
Management
Compliance
Management
Standards & Regulations
Information Management
Interpretation
Standards
Specification
Privacy
Assessment
(Independent)
Privacy Assessment
Product Engineering
“Project”
Quality
Management
Implementation
Validation &
Verification
Design
Model-based solutions for Privacy assurance projects
8. Results
Modelling privacy regulations as reference framework models
Diversity of reference frameworks
Process-based
Requirements-based
Evidence-based
Legal text
Objectives for modelling: Abstraction and Formalization
WP6
8
Privacy reference frameworks modelled as development processes
General and Application-domain-specific
9. Results
Handling ecosystems of privacy reference frameworks
Several privacy reference frameworks apply (and increasing)
WP6
9
Mapping models
GDPR
Art. 35 and 36: Data
protection impact
assessment and prior
consultation
Data Protection Impact
Assessment template
for Smart Grid and
Smart Metering
ISO/IEC 29134
Information technology —
Security techniques —
Guidelines for privacy
impact assessment
11. Results
Providing reusable privacy assurance patterns
Patterns:
the process of the ref framework is followed
the expected evidences are considered
to connect privacy controls with its expected assurance needs
Reusable privacy assurance patterns contain conditions and parts to be refined
They need to be instantiated and refined
WP6
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Manually created knowledge base, and automatic model transformations
15. Results: Knowledge base
Privacy Reference Frameworks
General
GDPR Data Protection Impact Assessments (DPIA) covering Art. 35 and 36, and WP29 DPIA guidance
ISO/IEC 29134:2017 (Information technology - Security techniques - Guidelines for privacy impact
assessment)
Case studies
ISO/SAE 21434 Road vehicles — Cybersecurity engineering. Process for risk assessment
EU Smart Grid Data Protection Impact Assessment (DPIA) template
WP6
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16. Results: Knowledge base
Mapping models
ISO/IEC 29134:2017 (Information technology - Security techniques - Guidelines for privacy
impact assessment)
to GDPR Data Protection Impact Assessments (DPIA)
D7.9 Alignment of Smart Grid DPIA to GDPR DPIA and ISO/IEC 29134:2017
WP6
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17. Results: Knowledge base
Privacy Assurance Patterns
General
GDPR DPIA argumentation patterns (13 based on Recital 75, Art. 35 and 36)
NIST SP 800-53 rev 5, Control SI-18 - Information disposal
NIST SP 800-53 rev 5, Control SI-20 - De-identification
Case studies
Connected vehicle: Correct pseudonym management (internally using NIST Control SI-20 pattern)
SmartGrid: Pre-assessment on the need to conduct a DPIA is completed. Smart Grid DPIA template
Automatically generated assurance patterns: e.g., Data Protection Risk Assessment is completed from
SmartGrid DPIA template reference framework
WP6
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18. Results: Knowledge base
Public
https://gitlab.eclipse.org/eclipse/opencert/opencert/-
/tree/release/2.0/examples/privacy
Private
Reference frameworks and mapping models of standards or documents which are not
freely distributed: Derivative works with high amount of information and text from the
original work
ISO/IEC 29134:2017 and its mapping model to GPDR
ISO/SAE 21434
WP6
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19. Conclusions and further steps beyond the
project
WP6
19
Using state-of-the-art assurance
principles for privacy engineering
Modelling privacy regulations as
reference framework models
Handling ecosystems of privacy
reference frameworks
Providing reusable privacy
assurance patterns
Tool-supported
Model-based
Open source and flexible
KB available
Community uptake (Industry and Research)
More automation support
20. Methods and Tools for GDPR Compliance through
Privacy and Data
Protection 4 Engineering
For more information, visit:
www.pdp4e-project.eu
Thank you for your attention
This project has received funding from the European Union's Horizon 2020
programme under Grant Agreement No 787034.