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dans.knaw.nl
DANS is een instituut van KNAW en NWO
GDPRov: provenance for GDPR
Vyacheslav Tykhonov
Senior Information Scientist
Research and Innovation department (DANS)
CLARIAH provenance workshop,
03.09.2018
General Data Protection Regulation (GDPR)
Specific requirements that important for scientific workflows:
Article 4: definition of Personal Data
Article 7: condition of concent for data collections
Article 9: processing of special categories and Personal Data
Article 30: Records of processing activities
Personal data questions
• Where? (storage)
• What? (metadata)
• Who? (owner)
• Why? (purpose)
• When? (date)
• How? (concent)
GDPR compliance
You need to implement specific workflows to be GDPR
compliant:
• Data management (practice of organizing and maintaining
data processes)
• Data governance (overall management of the availability,
Maintaining “right to be forgotten”
“The right to erasure or right to be forgotten grants data subjects a possibility to have their
personal data deleted if they don’t want them processed anymore and when there is no
legitimate reason for a data controller to keep it” (article 17)
Main challenges:
• problematic for highly connected or interlinked data
• Not always possible to execute data erasure (for example, if
data stored in blockchain)
The role of concent in GDPR
• Concent is an permission or agreement to keep Personal Data,
it can change its status in time
• In the case of change of consent, the previous consent must be
archived
• Using provenance metadata, it can be determined which version
of terms and conditions was used to get consent from user
Data storage according to GDPR
• Consent must be obtained with intended duration of storage
for data, indefinite storage isn’t allowed any more
• Data controller must renew the content to continue storing
data
• Provenance metadata must include anonymisation (Amnesia
tool as example) and archival activities
GDPRov - GDPR Provenance Ontology
“GDPRov is an OWL2 ontology for representing the
provenance of consent & data lifecycles with a view towards
GDPR compliance.
The ontology aims to provide a high-level abstraction of the
interactions of processes over consent & data.”
GDPRov was introduced in 2017 and extends PROV-O
standard and Ontology for Provenance and Plans called P-Plan.
Harshvardhan J. Pandit and Dave Lewis: Modelling provenance
for GDPR compliance using linked open data vocabularies
P-Plan ontology for activities
• Extends PROV-O to define software workflows or pipelines as
plans and link them to their past executions
• Uses “steps” and “variables” to explain how something is
happening
• Allows to create templates for execution with defined inputs
and outputs
• P-Plan usage helps to track changes in activities along how
they interact with consent and data
GDPRov vocabulary hierarchy
GDProv SPARQL query with consent
Questions?

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GDPRov: provenance for GDPR

  • 1. dans.knaw.nl DANS is een instituut van KNAW en NWO GDPRov: provenance for GDPR Vyacheslav Tykhonov Senior Information Scientist Research and Innovation department (DANS) CLARIAH provenance workshop, 03.09.2018
  • 2. General Data Protection Regulation (GDPR) Specific requirements that important for scientific workflows: Article 4: definition of Personal Data Article 7: condition of concent for data collections Article 9: processing of special categories and Personal Data Article 30: Records of processing activities
  • 3. Personal data questions • Where? (storage) • What? (metadata) • Who? (owner) • Why? (purpose) • When? (date) • How? (concent)
  • 4. GDPR compliance You need to implement specific workflows to be GDPR compliant: • Data management (practice of organizing and maintaining data processes) • Data governance (overall management of the availability,
  • 5. Maintaining “right to be forgotten” “The right to erasure or right to be forgotten grants data subjects a possibility to have their personal data deleted if they don’t want them processed anymore and when there is no legitimate reason for a data controller to keep it” (article 17) Main challenges: • problematic for highly connected or interlinked data • Not always possible to execute data erasure (for example, if data stored in blockchain)
  • 6. The role of concent in GDPR • Concent is an permission or agreement to keep Personal Data, it can change its status in time • In the case of change of consent, the previous consent must be archived • Using provenance metadata, it can be determined which version of terms and conditions was used to get consent from user
  • 7. Data storage according to GDPR • Consent must be obtained with intended duration of storage for data, indefinite storage isn’t allowed any more • Data controller must renew the content to continue storing data • Provenance metadata must include anonymisation (Amnesia tool as example) and archival activities
  • 8. GDPRov - GDPR Provenance Ontology “GDPRov is an OWL2 ontology for representing the provenance of consent & data lifecycles with a view towards GDPR compliance. The ontology aims to provide a high-level abstraction of the interactions of processes over consent & data.” GDPRov was introduced in 2017 and extends PROV-O standard and Ontology for Provenance and Plans called P-Plan. Harshvardhan J. Pandit and Dave Lewis: Modelling provenance for GDPR compliance using linked open data vocabularies
  • 9. P-Plan ontology for activities • Extends PROV-O to define software workflows or pipelines as plans and link them to their past executions • Uses “steps” and “variables” to explain how something is happening • Allows to create templates for execution with defined inputs and outputs • P-Plan usage helps to track changes in activities along how they interact with consent and data
  • 11. GDProv SPARQL query with consent