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Personal Data Privacy Semantics in
Multi-Agent Systems Interactions
Davide Calvaresi • Michael Schumacher • Jean-Paul Calbimonte
University of Applied Sciences and Arts Western Switzerland (HES-SO)
International Conference on Practical Applications of Agents and Multi-Agent Systems – PAAMS 2020
October 2020
@jpcik
2
HES-SO:
University of Applied Sciences and Arts Western Switzerland
3
Motivation: Personal data protection
demographics diagnosis morbidities
mobile data
self-reported
Personal data
sensor data
Decentralizedhealthcare
Who owns the data?
Who manages the data?
Who grants access to the data?
Who can contribute to the data?
Who can transfer the data?
Who can process the data?
Complex compliance restrictions
Institutional boundaries
Participatory data collection
Decentralized decision-makingChallenges
4
What this paper is about
o personal data privacy interaction requirements
o design principles of privacy-aware agent interactions
o conceptual architecture
o multi-agent protocol specifications
o semantic information: purpose – recipient – processing - consent
decentralized agent-based data privacy
negotiation
coordination
enforcement
semantic
representations
privacy conditions/handling
A B
Next →
5
Use-case: digital rehabilitation
θ
request aggregated
sensor data
data access consent
request access
request access
accept
reject
motion
monitoring
sensors
knee rehabilitation exercises
track exercise and physical activity
Patient-centric data control?
Personal data reuse?
Data sharing negotiation?
6
Information needs
o timely access all collected data during the interventions?
o opt-out of specific processing/monitoring activities?
o establish restrictions on types of data to be collected/reused?
o trace the actions and data access of healthcare providers?
o limit read/write access to specific healthcare providers?
o delete or withdraw her data completely or partially?
o change consent conditions/restrictions on data handling purposes?
o be notified of risks/evidence of privacy breach or undesired activities?
Can the
subject …
7
Requirements
R1: Data handling actors
R2: Decentralized interactions
R3: Semantic data privacy modelling
R4: Interaction protocols
R5: Legal compliance
establish shared understanding of data handling actors:
data controllers, subjects, recipients
specify possible interactions among data handling actors
without a centralized entity governing their decisions.
rely on standard semantic models that represent data
handling purposes, processes, consent, privacy
follow a well-defined interaction pattern, specified as a set
of behaviors, allowing negotiation / collaboration
comply with the applicable legal
framework, e.g., GDPR
8
Requirements
R6: Verification
R7: Tracking
R8: Explainability
R9: Transparency
R10: Granularity
verify the compliance to regulations across institutional
boundaries
keep track of all interactions, reuse, access, processing,
and handling events
controllers expose explainable and understandable
interfaces for all data handling processes.
controllers timely communicate any event concerning data
privacy, such as risks, breaches, compromises, etc.
choose the granularity at which personal data
handling is performed
9
Design Principles
decentralized agents
Data autonomy
Goal setting
Policies
Consent conditions
Data quality
Anonymization
Negotiation protocols
Collaboration patterns
Data tracking petition
Data exclusion requests
semantic representation
knowledge
beliefs
goals
Privacy
specifications
Privacy ontologies
10
Decentralized Agents
Controllers
Subjects
Recipients
Processors
people, organizations, or authorities that govern and
decide about the purpose and processing of personal data
persons to which the data is related
people or entities to which the personal information
is disclosed
persons or entities that perform any processing of
the personal data on behalf of the controller
11
Decentralized Agents
Subject Subject
Processor
Recipient
consent
consent
Controller
analytics request
12
Shared semantic vocabularies
Processing
PersonalData
Handling
PersonalDataCategory
hasPersonalDataCategory
Purpose
LegalBasis
DataController
Recipient
DataSubject
TechnicalOrganisationalMeasure
hasRecipient
establishment of interactions among decentralized agents
common model for representing privacy data
Data Privacy Vocabulary (DPV): W3C Data Privacy Vocabularies and Control Community Group
https://www.w3.org/ns/dpv
13
Data privacy agent interactions
– Controller requests personal data (with consent) to a specific subject.
– Subject provides personal data (with a consent granted).
– Controller calls for personal data to a set of individuals represented by their subject agents.
– Subject selects only a certain purpose for data handling.
– Subject rejects request for data.
– Subject grants access to personal data only for a certain purpose.
– Subject/Controller customizes permissions and access rights.
– Controller tracks personal data reuse and processing.
– Subject deletes or withholds own personal data.
– Subject/controller verifies personal data use and policy
– Subject objects to data reuse or processing.
– Subject requests access to own personal data collected (and metadata)
– Controller notifies about data breaches or risk.
non-comprehensive minimum set of interactions:
14
Data privacy agent interactions
Controller Subject
request access
refuse
agree
failure
inform-done
inform-result
[refused]
[agreed and notification necessary]
[agreed]
consent
Controller Subject
Call-for-data
refuse
propose
inform-done
inform-result
reject-proposal
accept-proposal
failure
consent
15
Semantic representation of interactions
{
"prov:generatedAtTime": "2020-02-01T04:00:00.000Z",
"@id": "ex:callForActivityData",
"@graph": [
{ "@id": "ex:callForData1",
"ag:permormative": "ag:CallForProposals",
"ag:sender": "ex:controller1",
"ag:protocol": "ag:ContractNet",
"ag:ontology": "http://w3id.org/ns/dpv#",
"ag:content": "ex:consentPatient1"
}
]
}
Data request
through a bid
FIPA protocol
Proposed
consent
JSON-LD representation
16
Example: Data collection
ex:dataRequest a dpv:PersonalDataHandling ;
dpv:hasDataSubject ex:patient1 ;
dpv:hasPurpose [a dpv:AcacemicResearch] ;
dpv:hasProcessing [a dpv:Collect];
dpv:hasLegalBasis [a dpv:Consent];
dpv:hasDataController ex:hospital1;
dpv:haRecipient ex:physician3;
dpv:hasPersonalDataCategory [a dpv:PhysicalHealth];
dcterms:title "Personal Data Collection for clinical study ..."
.
patient subject
controller
17
Example: Consent
ex:consentPatient1 a dpv:Consent ;
dpv:hasDataSubject ex:patient1 ;
dpv:hasPurpose [a dpv:AcacemicResearch],
[a dpv:CommercialResearch],
[a dpv:CreatePersonalizedRecommendations] ;
dpv:hasProcessing [a dpv:Analyse];
dcterms:title "Consent for Health data analysis in a clinical study ..." ;
dpv:hasDataController ex:hospital1;
dpv:haRecipient ex:physiotherapist1;
dpv:hasPersonalDataCategory [a dpv:PhysicalHealth].
Consent
purposes
Data
recepient
18
Example: Processing
ex:dataAnalysis a dpv:Analysis ;
dpv:hasDataSubject ex:patient1 ;
prov:used ex:patientDataset1 ;
dcterms:title "Data Analysis activity for patient data ..." ;
prov:isAssociatedWith ex:dataScientist1;
prov:wasStartedAtTime "2020-01-11T04:00:00.000Z".
ex:analyticsResults a prov:Entity ;
prov:wasGeneratedBy ex:dataAnalysis;
prov:wasDerivedFrom ex:patientDataset1
19
Related work
20
Conclusions
semantic data models
A vision for decentralized personal data privacy interactions.
tackle current regulations such as the GDPR.
autonomy
decentralization
negotiation
interactions
multi-agent systems
DPV ontology
heterogeneity
privacy policies
consent
Future work
o Domain-specific vocabularies/ontologies that describe detailed data processing
conditions, purposes and data handling policies
o Development of multi-agent environments that implement the interactions,
deployable in mobile and sensing devices.
o Study and implementation of agent negotiation protocols for personal data privacy
workflows.
o Specification and validation of consent and policies for data privacy, checking for
compliance with regulations.
o The validation and evaluation of the proposed model, real environment
¿questions?
Jean-Paul Calbimonte
University of Applied Sciences and Arts Western Switzerland
HES-SO Valais-Wallis
@jpcik

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Personal Data Privacy Semantics in Multi-Agent Systems Interactions

  • 1. Personal Data Privacy Semantics in Multi-Agent Systems Interactions Davide Calvaresi • Michael Schumacher • Jean-Paul Calbimonte University of Applied Sciences and Arts Western Switzerland (HES-SO) International Conference on Practical Applications of Agents and Multi-Agent Systems – PAAMS 2020 October 2020 @jpcik
  • 2. 2 HES-SO: University of Applied Sciences and Arts Western Switzerland
  • 3. 3 Motivation: Personal data protection demographics diagnosis morbidities mobile data self-reported Personal data sensor data Decentralizedhealthcare Who owns the data? Who manages the data? Who grants access to the data? Who can contribute to the data? Who can transfer the data? Who can process the data? Complex compliance restrictions Institutional boundaries Participatory data collection Decentralized decision-makingChallenges
  • 4. 4 What this paper is about o personal data privacy interaction requirements o design principles of privacy-aware agent interactions o conceptual architecture o multi-agent protocol specifications o semantic information: purpose – recipient – processing - consent decentralized agent-based data privacy negotiation coordination enforcement semantic representations privacy conditions/handling A B Next →
  • 5. 5 Use-case: digital rehabilitation θ request aggregated sensor data data access consent request access request access accept reject motion monitoring sensors knee rehabilitation exercises track exercise and physical activity Patient-centric data control? Personal data reuse? Data sharing negotiation?
  • 6. 6 Information needs o timely access all collected data during the interventions? o opt-out of specific processing/monitoring activities? o establish restrictions on types of data to be collected/reused? o trace the actions and data access of healthcare providers? o limit read/write access to specific healthcare providers? o delete or withdraw her data completely or partially? o change consent conditions/restrictions on data handling purposes? o be notified of risks/evidence of privacy breach or undesired activities? Can the subject …
  • 7. 7 Requirements R1: Data handling actors R2: Decentralized interactions R3: Semantic data privacy modelling R4: Interaction protocols R5: Legal compliance establish shared understanding of data handling actors: data controllers, subjects, recipients specify possible interactions among data handling actors without a centralized entity governing their decisions. rely on standard semantic models that represent data handling purposes, processes, consent, privacy follow a well-defined interaction pattern, specified as a set of behaviors, allowing negotiation / collaboration comply with the applicable legal framework, e.g., GDPR
  • 8. 8 Requirements R6: Verification R7: Tracking R8: Explainability R9: Transparency R10: Granularity verify the compliance to regulations across institutional boundaries keep track of all interactions, reuse, access, processing, and handling events controllers expose explainable and understandable interfaces for all data handling processes. controllers timely communicate any event concerning data privacy, such as risks, breaches, compromises, etc. choose the granularity at which personal data handling is performed
  • 9. 9 Design Principles decentralized agents Data autonomy Goal setting Policies Consent conditions Data quality Anonymization Negotiation protocols Collaboration patterns Data tracking petition Data exclusion requests semantic representation knowledge beliefs goals Privacy specifications Privacy ontologies
  • 10. 10 Decentralized Agents Controllers Subjects Recipients Processors people, organizations, or authorities that govern and decide about the purpose and processing of personal data persons to which the data is related people or entities to which the personal information is disclosed persons or entities that perform any processing of the personal data on behalf of the controller
  • 12. 12 Shared semantic vocabularies Processing PersonalData Handling PersonalDataCategory hasPersonalDataCategory Purpose LegalBasis DataController Recipient DataSubject TechnicalOrganisationalMeasure hasRecipient establishment of interactions among decentralized agents common model for representing privacy data Data Privacy Vocabulary (DPV): W3C Data Privacy Vocabularies and Control Community Group https://www.w3.org/ns/dpv
  • 13. 13 Data privacy agent interactions – Controller requests personal data (with consent) to a specific subject. – Subject provides personal data (with a consent granted). – Controller calls for personal data to a set of individuals represented by their subject agents. – Subject selects only a certain purpose for data handling. – Subject rejects request for data. – Subject grants access to personal data only for a certain purpose. – Subject/Controller customizes permissions and access rights. – Controller tracks personal data reuse and processing. – Subject deletes or withholds own personal data. – Subject/controller verifies personal data use and policy – Subject objects to data reuse or processing. – Subject requests access to own personal data collected (and metadata) – Controller notifies about data breaches or risk. non-comprehensive minimum set of interactions:
  • 14. 14 Data privacy agent interactions Controller Subject request access refuse agree failure inform-done inform-result [refused] [agreed and notification necessary] [agreed] consent Controller Subject Call-for-data refuse propose inform-done inform-result reject-proposal accept-proposal failure consent
  • 15. 15 Semantic representation of interactions { "prov:generatedAtTime": "2020-02-01T04:00:00.000Z", "@id": "ex:callForActivityData", "@graph": [ { "@id": "ex:callForData1", "ag:permormative": "ag:CallForProposals", "ag:sender": "ex:controller1", "ag:protocol": "ag:ContractNet", "ag:ontology": "http://w3id.org/ns/dpv#", "ag:content": "ex:consentPatient1" } ] } Data request through a bid FIPA protocol Proposed consent JSON-LD representation
  • 16. 16 Example: Data collection ex:dataRequest a dpv:PersonalDataHandling ; dpv:hasDataSubject ex:patient1 ; dpv:hasPurpose [a dpv:AcacemicResearch] ; dpv:hasProcessing [a dpv:Collect]; dpv:hasLegalBasis [a dpv:Consent]; dpv:hasDataController ex:hospital1; dpv:haRecipient ex:physician3; dpv:hasPersonalDataCategory [a dpv:PhysicalHealth]; dcterms:title "Personal Data Collection for clinical study ..." . patient subject controller
  • 17. 17 Example: Consent ex:consentPatient1 a dpv:Consent ; dpv:hasDataSubject ex:patient1 ; dpv:hasPurpose [a dpv:AcacemicResearch], [a dpv:CommercialResearch], [a dpv:CreatePersonalizedRecommendations] ; dpv:hasProcessing [a dpv:Analyse]; dcterms:title "Consent for Health data analysis in a clinical study ..." ; dpv:hasDataController ex:hospital1; dpv:haRecipient ex:physiotherapist1; dpv:hasPersonalDataCategory [a dpv:PhysicalHealth]. Consent purposes Data recepient
  • 18. 18 Example: Processing ex:dataAnalysis a dpv:Analysis ; dpv:hasDataSubject ex:patient1 ; prov:used ex:patientDataset1 ; dcterms:title "Data Analysis activity for patient data ..." ; prov:isAssociatedWith ex:dataScientist1; prov:wasStartedAtTime "2020-01-11T04:00:00.000Z". ex:analyticsResults a prov:Entity ; prov:wasGeneratedBy ex:dataAnalysis; prov:wasDerivedFrom ex:patientDataset1
  • 20. 20 Conclusions semantic data models A vision for decentralized personal data privacy interactions. tackle current regulations such as the GDPR. autonomy decentralization negotiation interactions multi-agent systems DPV ontology heterogeneity privacy policies consent
  • 21. Future work o Domain-specific vocabularies/ontologies that describe detailed data processing conditions, purposes and data handling policies o Development of multi-agent environments that implement the interactions, deployable in mobile and sensing devices. o Study and implementation of agent negotiation protocols for personal data privacy workflows. o Specification and validation of consent and policies for data privacy, checking for compliance with regulations. o The validation and evaluation of the proposed model, real environment
  • 22. ¿questions? Jean-Paul Calbimonte University of Applied Sciences and Arts Western Switzerland HES-SO Valais-Wallis @jpcik