Privacy policy information in data value chains
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Privacy policy information in data value chains Sabrina Kirrane (Senior researcher, Vienna University)
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Privacy policy information in data value chains
- 1. (Privacy) policy information in data value chains Sabrina Kirrane Vienna University of Economics and Business
- 2. Companies whose business models rely on personal data and for which the GDPR is both a challenge and an opportunity Data subjects who would like to declare, monitor and optionally revoke their (often not explicit) preferences on data sharing Regulators who can leverage technical means to check compliance with the GDPR 2013 2014 2015 2016 2017 2018 Draft of the regulation 7/22/2012 Revisions in the draft 3/12/2013 Discussions in the EU Council 5/19/2014 EU Council finalises the chapters 8/6/2015 Trilogue starts 6/24/2015 Trilogue agrees 12/17/2015 Comes into effect 5/25/2018 Scalable Policy-awarE Linked Data arChitecture for prIvacy, trAnsparency and compLiance (SPECIAL) 2 2019 SPECIAL Kickoff Jan 2017 SPECIAL Ends Dec2019
- 3. http://themerkle.com/slur-io/ Data Market Data Lake The Big Data Ecosystem Data Spaces https://www.internationaldataspaces.org/wp- content/uploads/dlm_uploads/2019/07/20190625-1500-Common-European- Industrial-IoT-by-Arian-Zwegers.pdf https://solutionsreview.com/data-integration/the-emergence-of-data-lake-pros-and-cons/ How will the GDPR effect my fishing?
- 4. Big Data & Anonymisation Primary challenges: It is hard to give guarantees with respect to anonymity There is a tradeoff between anonymity and utility
- 5. Data & Data Driven Services Regulators Companies/ Service Providers Customers/ Service Users Privacy Preferences Legal Policies Contracts/ Terms of use Data & Usage Constraints 5 Primary challenges: Policy specification Automated compliance checking
- 6. The Artificial Intelligence Ecosystem http://publications.jrc.ec.europa.eu/repository/b itstream/JRC113826/ai-flagship-report- online.pdf Apple's 1987 Knowledge Navigator https://commons.wikimedia.org/wiki/File:Knowledge _Navigator.jpg Nora Ni Loideain, Conversational Agents: Siri, Alexa, & Cortana https://infolawcentre.blogs.sas.ac.uk/about/dr-nora-ni- loideain/
- 7. Data Value Chains SPECIAL Technical Foundations
- 8. Data & Data Driven Services The World Wide Web 1989 The original proposal for the Web https://www.w3.org/History/1989/proposal.html
- 9. Data & Data Driven Services How can we ensure Interoperability? • Globally Unique identifiers • A common protocol • Links between Documents URIs HTTP href
- 10. Data & Data Driven Services The Semantic Web & Intelligent Agents 2001 The Semantic Web https://www.scientificamerican.com Image by Radar Networks; Nova Spivak http://memebox.com/futureblogger/show/824
- 11. www.sabrinakirrane.com#me foaf: workplaceHomepage https://www.wu.ac.at/en/Infobiz/ • Globally Unique identifiers • A common protocol • Links between Documents • Globally Unique identifiers • A common protocol • Typed Links between Entities URIs HTTP href URIs HTTP RDF Data & Data Driven Services How can we ensure Interoperability? https://www.wu.ac.at/en/Infobiz/
- 12. www.axelpolleres.com#me foaf: workplaceHomepage https://www.wu.ac.at/en/Infobiz/ www.sabrinakirrane.com#me foaf: workplaceHomepage https://www.wu.ac.at/en/Infobiz/ www.sabrinakirrane.com#me foaf: knows www.axelpolleres.com#me • Globally Unique identifiers • A common protocol • Links between Documents • Globally Unique identifiers • A common protocol • Typed Links between Entities URIs HTTP href URIs HTTP RDF Data & Data Driven Services How can we ensure Interoperability?
- 13. www.sabrinakirrane.com#me foaf: workplaceHomepage https://www.wu.ac.at/en/Infobiz/ www.axelpolleres.com#me • Globally Unique identifiers • A common protocol • Links between Documents • Globally Unique identifiers • A common protocol • Typed Links between Entities URIs HTTP href HTTP RDF URIs Data & Data Driven Services How can we ensure Interoperability?
- 14. www.sabrinakirrane.com#me foaf: workplaceHomepage https://www.wu.ac.at/en/Infobiz/ www.axelpolleres.com#me • Globally Unique identifiers • A common protocol • Links between Documents • Globally Unique identifiers • A common protocol • Typed Links between Entities URIs HTTP href HTTP RDF URIs Data & Data Driven Services How can we ensure Interoperability? • Common data model for encoding data (triples) • Common ways of serialising data (syntaxes) • Well-defined languages for saying what terms mean (semantics) • Common ways to query data (query languages)
- 15. Data & Data Driven Services Distributed Data Sources https://www.europeandataportal.eu/en/homepage When it comes to datasets this is just the tip of the iceberg….
- 16. Data & Data Driven Services Privacy Preferences Data & Data Driven Services Regulators Companies/ Service Providers Customers/ Service Users Privacy Preferences Legal Policies Contracts/ Terms of use
- 17. • Available for download via the SPECIAL website: https://www.specialprivacy.eu/publications/pu blic-deliverables • An unofficial draft specification has been published online https://www.specialprivacy.eu/platform/ontolo gies-and-vocabularies Privacy Preferences How can we ensure Interoperability?
- 18. Privacy Preferences Challenge: Human Computer Interaction We need to make it easy for individuals to manage their personal data
- 19. Data & Data Driven Services Terms of Use Data & Data Driven Services Regulators Companies/ Service Providers Customers/ Service Users Privacy Preferences Legal Policies Contracts/ Terms of use
- 20. • Modeling licenses using the Open Digital Rights Language • Dependency modeling • Conflict detection & Resolution Automated Rights Clearance Using Semantic Web Technologies: The DALICC Framework, Tassilo Pellegrini, Victor Mireles, Simon Steyskal, Oleksandra Panasiuk, Anna Fensel, and Sabrina Kirrane, Semantic Applications: Methodology, Technology, Corporate Use, Springer Semantic Applications, Methodology, Technology, Corporate Use, 2018 W3C Recommendation Contracts & Terms of Use How can we ensure Interoperability?
- 21. https://en.wikipedia.org/wiki/File:Data_for_2015_stampede_analysis.jpg https://www.flickr.com/photos/ruiwen/3260095534 Contracts & Terms of Use Challenge: Compliance There are many resources without any terms of us We need compliance tools
- 22. Data & Data Driven Services Legal Policies Data & Data Driven Services Regulators Companies/ Service Providers Customers/ Service Users Privacy Preferences Legal Policies Contracts/ Terms of use
- 23. • Modeling regulatory obligations using an adaption of the Open Digital Rights Language • Automated compliance checking for business policies ODRL policy modelling and compliance checking, Marina De Vos, Sabrina Kirrane, Julian Padget and Ken Satoh, Proceedings of the 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019) Draft Specification Legal Policies How can we ensure Interoperability?
- 24. • Extracting temporal and event data from legal text • Modeling metadata relating to legislation and cases in a legal knowledge graph Interlinking Legal Data, Erwin Filtz, Sabrina Kirrane and Axel Polleres, Proceedings of the Posters and Demos Track of the 14th International Conference on Semantic Systems (SEMANTiCS 2018) Multilingual Cross Jurisdiction Compliance Tools Legal Policies Challenge: Cross Jurisdiction
- 25. Data & Data Driven Services Challenges & Opportunities
- 26. • Standardisation of vocabularies (e.g., privacy, legal, licensing) is difficult • Privacy is only the tip of the iceberg, from a usage control perspective we also need to consider other regulations, licenses, social norms, cultural differences • There are cognitive limitations in terms of understanding how data is /will be used • Ensuring such systems are comply with usage constraints is a crucial to success (i.e., all usage policies are adhered to and the system as a whole works as expected) • We need to embrace distributed and decentralised systems, which complicates things further Data Value Chains Challenges & Opportunities
- 27. Thank you / contact details Technical/Scientific contact Sabrina Kirrane Vienna University of Economics and Business sabrina.kirrane@wu.ac.at Administrative contact Jessica Michel Assoumou ERCIM W3C jessica.michel@ercim.eu The project SPECIAL (Scalable Policy-awarE linked data arChitecture for prIvacy, trAnsparency and compLiance) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731601 as part of the ICT-18-2016 topic Big data PPP: privacy-preserving big data technologies.
Editor's Notes
- Firstly what is SPECIAL SPECIAL stands for Scalable Policy Linked Data Architecture for Privacy, Transparency & Compliance In SPECIAL we are: - enabling data subjects to have more control over how their personal data is handled - enabling regulators to verify that companies are complying with the GDPR - enabling companies to continue to innovate in light of the GDPR ....
- First I want to set the scene…. When SPECIAL started in 2017 there was a lot of talk about Data Lakes….. And companies were particularly interested using data analytics and data science in order to uncover new insights from data and develop innovative projects and services.... There has also been work on how to better leverage our data assets, for instance via data markets and data spaces Making it easier for product and service providers to get access to good quality data Pushing research and innovation forward by builing domain specific data spaces
- There are many people that believe that anonymisation and data synthesis is the way forward….. Of course this is very attractive, especially considerng that according to the GDPR – The principles of data protection do not apply to anonymous data However there are two primary challenges here: Primary challenges: It is hard to give guarantees with respect to anonymity especially when data is aggregated There is a tradeoff between anonymity and utility
- So what is the alternative…... Essentially in SPECIAL we believe that metadata in the form of constraints is the answer….. In this figure we see the three different stakeholder groups: Data Subjects – Custoers & Service Users – who want to specify their privacy preferences Regulators – legal policies e.g. GDPR Requirements Companies - Service Providers – who need to specify contracts and terms of use
- Although SPECIAL is not an AI project, in keeping with the AI and Interoperability theme of this session in this talk I plan to give an indication as to how the technical foundations of SPECIAL could underpin internet of things, cyber physical social system, autonomous cars…. In this talk we will focus on intelligent agents,
- The figure on the left is the original proposal for the Web by Tim Berners Lee
- One option would be to leverage Semantic Web Technology The Resource Description Framework (RDF), which underpins the Semantic Web, is used to represent and link information, in a manner which can be interpreted by both humans and machines. The Semantic Web is woven into the fabric of the Web Explain URIs, HTTP, & HREF
- The figure on the left is the original proposal for the Web by Tim Berners Lee
- The Resource Description Framework (RDF), which underpins the Linked Data Web (LDW), is used to represent and link information, in a manner which can be interpreted by both humans and machines.
- Essentially it is a tuple composed of three attributes: Subject, Predicate and Object
- When combined the attributes form a graph The usage of RDF and URIs shall enable the deployment of a linked network of distributed ledgers instead of a single, monolithic (central or P2P ledger). Here it would be interesting to look into efforts for modularising and linking between distributed ledgers such as the recent interledger protocol [8] proposal.
- When combined the attributes form a graph The usage of RDF and URIs shall enable the deployment of a linked network of distributed ledgers instead of a single, monolithic (central or P2P ledger). Here it would be interesting to look into efforts for modularising and linking between distributed ledgers such as the recent interledger protocol [8] proposal.