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An itinerary for FAIR and privacy respecting data-driven innovation and research

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My talk for the National eScience Symposium 2017 in the Internet of Things track, October 12 2017.

TALK: An itinerary for FAIR and privacy respecting data-driven innovation and research

ABSTRACT: The big picture of the complex landscape of e-science, technology, legal and ethical responsibilities addressed. How to apply privacy values and responsibilities to new technological platforms like the IoT? Can we find an approach that ensures a high level of privacy protection and at the same time supports the interest of researchers and increase innovation? A practical recap of the most important recommendations for researchers creating collaborations and infrastructures.

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An itinerary for FAIR and privacy respecting data-driven innovation and research

  1. 1. An itinerary for FAIR and privacy respecting data-driven innovation and research National eScience Symposium 2017 October 12, Amsterdam Marlon Domingus “Nowadays people know the privacy risk of everything and the value of nothing.” variation on an Oscar Wilde theme
  2. 2. Itinerary 1. On the EU General Data Protection Regulation 2. On Governance of Re-Use of Data 3. Privacy in the context of Research and Big Data Research 4. Privacy and the Internet of Things 5. Next Steps 2
  3. 3. The GDPR Elephant Is In The Room Image sources: Top, Bottom left side, Bottom right side. Will you ignore it and allow it to become your weakness? Or will you adapt to it and make it your strength to safeguard privacy? Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl September 2017 General Data Protection Regulation Disclaimer
  4. 4. EU General Data Protection Regulation What, Who, How, When What & How 25 May 2018October 2017 15 December 2017 Who University: provide necessary general conditions to enable researchers to comply; policy, guidelines, infrastructure and skilled and available research support staff. Dean: provide additional necessary discipline specific conditions to enable researchers to comply; policy, guidelines, infrastructure and skilled and available research support staff. Faculty: follow privacy principles & use the privacy enabling conditions (policy, guidelines, infrastructure and skilled and available research support staff).
  5. 5. Does Privacy Threaten Research and / or Does Research Threaten Privacy? • The EU General Data Protection Regulation (GDPR) is principle based • intended to facilitate the responsible free floating of data within the EU to strengthen the internal market, especially by public - private driven innovation. • The Right to Privacy is not an absolute right, but a fundamental right amongst other rights. • Conclusion: no business as usual, but also no disruption of research. • GDPR is a game changer, and we have to shift to the new paradigm and govern research in a new way. 5
  6. 6. Command & Control • Fixed norm • Actor • Sanction • Example: METC Reflexive regulation • Situated norm • Multiple Actors • Learn • Example: intervision Two Models of Governance Source: Prof. Dr. Antoinette de Bont, Erasmus School of Health Policy & Management (ESHPM): The Governance of re-use of data. Summerschool 9-12 July 2017 @ Erasmus MC.
  7. 7. Reflexive Governance of Re-Use of Data Source: Prof. Dr. Antoinette de Bont, Erasmus School of Health Policy & Management (ESHPM): The Governance of re-use of data. Summerschool 9-12 July 2017 @ Erasmus MC. Set up research aimed to reduce margins of uncertainty; Set up research to detect vulnerabilities in the environment; Institute long‐term monitoring systems and facilities for early warnings of possible harmful effects. Invite stakeholders to contribute to strategic discussions about the research you do
  8. 8. Privacy Before Research: Research Design Result: Data Management Plan DPIA Risks, Appropriate Organisational and Technical Measures, Ethical Self Assessment 2 Data Management Plan See for DPIA: pg 14. Article 29 Data Protection Working Party: Guidelines on Data Protection Impact Assessment (DPIA) and determining whether processing is “likely to result in a high risk” for the purposes of Regulation 2016/679. Adopted on 4 April 2017. See: http://ec.europa.eu/newsroom/document.cfm?doc_id=44137 Privacy Principles 1
  9. 9. 1. The EU General Data Protection Regulation: Article 5 GDPR: Principles Relating to Processing of Personal Data Source: http://gdprcoalition.ie/infographics/
  10. 10. 2. The EU General Data Protection Regulation: Privacy Before, During and After Research Created in collaboration with the GDPR Coalition
  11. 11. Privacy Before Research: Privacy by Design Strategy (‘traditional’) Source: ENISA report (2015): Privacy By Design In Big Data. Online: https://www.enisa.europa.eu/publications/big-data-protection/at_download/fullReport 11
  12. 12. Privacy Before Research: Privacy by Design Strategy (Big Data) Source: ENISA report (2015): Privacy By Design In Big Data. Online: https://www.enisa.europa.eu/publications/big-data-protection/at_download/fullReport 12
  13. 13. Privacy Before Research: Privacy Enhancing Technologies in Big Data Anonymization in big data (and beyond) Utility and privacy Attack models and disclosure risk Anonymization privacy models Anonymization privacy models and big data Anonymization methods Some current weaknesses of anonymization Centralized vs decentralized anonymization for big data Other specific challenges of anonymization in big data Challenges and future research for anonymization in big data Encryption techniques in big data Database encryption Encrypted search Security and accountability controls Granular access control Privacy policy enforcement Accountability and audit mechanisms Data provenance Transparency and access Consent, ownership and control Consent mechanisms Privacy preferences and sticky policies Personal data stores Source: ENISA report (2015): Privacy By Design In Big Data. Online: https://www.enisa.europa.eu/publications/big-data-protection/at_download/fullReport 13
  14. 14. WP Art 29: Big Data Concerns: 14 - the sheer scale of data collection, tracking and profiling, also taking into account the variety and detail of the data collected and the fact that data are often combined from many different sources; - the security of data, with levels of protection shown to be lagging behind the expansion in volume; - transparency: unless they are provided with sufficient information, individuals will be subject to decisions that they do not understand and have no control over; - inaccuracy, discrimination, exclusion and economic imbalance; - increased possibilities of government surveillance. Source: Article 29 Data Protection Working Party. Opinion 03/2013 on purpose limitation. Adopted on 2 April 2013. Online: http://ec.europa.eu/justice/data-protection/article-29/documentation/opinion-recommendation/files/2013/wp203_en.pdf
  15. 15. Balancing the legitimate interests of the researcher and the privacy rights of the individual Source: Prof. Dr. Gloria González Fuster: Recent jurisprudence of the European Court of Human Rights and the Court of Justice of the European Union. Brussels Privacy Hub, VUB Brussel, June 30 2017. independent authority individual’s rights legitimate interests 15 GDPR Member States’ Implementation Legislation Codes of Conduct Discipline Specific Good Practices of the researcher of the data subject
  16. 16. Balancing: Four Steps 1. Legitimate interests of controller or 3rd party • freedom of expression • direct marketing and other forms of advertisement • enforcement of legal claims • prevention of fraud, misuse of services, or money laundering • physical safety, security, IT and network security • whistle-blowing schemes 2. Impact on data subject Actual and potential repercussions • Nature of the data • How the data are processed • Reasonable expectations data subject • Nature of controller vis-à-vis data subject 3. Make provisional balance “Necessary” • Least intrusive means • Reasonably effective • Balance of interests 4. Safeguards Measures to ensure that the data cannot be used to take decisions or other actions with regard to individuals. • anonymisation techniques, aggregation of data • privacy-enhancing technologies, privacy by design • increased transparency • general and unconditional right to opt-out Source: Article 29 Data Protection Working Party. Opinion 06/2014 on the "Notion of legitimate interests of the data controller under Article 7 of Directive 95/46/EC". Adopted on 9 April 2014. Online: http://ec.europa.eu/justice/data-protection/article-29/documentation/opinion-recommendation/files/2014/wp217_en.pdf 16
  17. 17. Privacy in the Context of Research: 9 Generic Research Scenarios
  18. 18. storing data analysing data deleting data archiving data for follow-on research own research publicly publishing data principal investigator academia Research Scenarios and the General Data Protection Regulation: 1. Individual Academic Research Based on Existing Data Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl August 24 2017 adequate organisational and technical measures accessing and collecting data existing data existing observed data online / sensor data
  19. 19. storing data analysing data deleting data archiving data for follow-on research own research publicly publishing data Research Scenarios and the General Data Protection Regulation: 2. Academic Research by an International Research Group Based on Existing Data Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl August 24 2017 adequate organisational and technical measures accessing and collecting data existing data existing observed data online / sensor data accessing data academic researcher academic researcher academic researcher sharing data for analysing, archiving and publishing purposes analysing data archiving data publishing data adequate organisational and technical measures principal investigator academia
  20. 20. storing data analysing data deleting data archiving data for follow-on research own research publicly publishing data principal investigator academia Research Scenarios and the General Data Protection Regulation: 3. Individual Academic Research Based on Generated Data from Data Subjects Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl August 24 2017 - informed consent - public interest generating new data data subjects data subject's rights: • to be informed • of access • to rectification • to erasure • to restriction of processing • to objection of processing • to data portability • to withdraw consent • to lodge a complaint to a supervisory authority • right not to be subject to a decision based solely on automated processing adequate organisational and technical measures
  21. 21. storing data analysing data deleting data archiving data for follow-on research own research publicly publishing data Research Scenarios and the General Data Protection Regulation: 4. Academic Research by an International Research Group Based on Generated Data from Data Subjects Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl August 24 2017 - informed consent - public interest generating new data data subjects adequate organisational and technical measures accessing data academic researcher academic researcher academic researcher sharing data for analysing, archiving and publishing purposes analysing data archiving data publishing data adequate organisational and technical measures principal investigator academia
  22. 22. storing data analysing data deleting data archiving data for follow-on research own research publicly publishing data principal investigator academia Research Scenarios and the General Data Protection Regulation: 5. Academic Research by an International Research Group Based on Generated Data from Data Subjects Combined With Existing Data Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl August 24 2017 - informed consent - public interest generating new data data subjects adequate organisational and technical measures accessing data academic researcher academic researcher academic researcher sharing data for analysing, archiving and publishing purposes analysing data archiving data publishing data adequate organisational and technical measures accessing and collecting data existing data existing observed data online / sensor data
  23. 23. storing data analysing data deleting data archiving data for follow-on research own research publicly publishing data Research Scenarios and the General Data Protection Regulation: 6. Academic Research by International Public - Private Research Group Based on Generated Data from Data Subjects Combined With Existing Data Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl August 24 2017 - informed consent - public interest generating new data data subjects adequate organisational and technical measures accessing data academic researcher academic researcher academic researcher sharing data for analysing, archiving and publishing purposes analysing data archiving data publishing data adequate organisational and technical measures accessing and collecting data existing data existing observed data online / sensor data non academic research partner non academic research partner principal investigator academia
  24. 24. storing data analysing data deleting data archiving data for follow-on research own research publicly publishing data principal investigator academia Research Scenarios and the General Data Protection Regulation: 7. Academic Research by International Public - Private Research Group Based on Generated Data from Data Subjects Combined With Existing Data and Licensed Data Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl August 24 2017 - informed consent - public interest generating new data data subjects adequate organisational and technical measures accessing data academic researcher academic researcher academic researcher sharing data for analysing, archiving and publishing purposes analysing data archiving data publishing data adequate organisational and technical measures accessing and collecting data existing data existing observed data online / sensor data non academic research partner non academic research partner licensed data
  25. 25. storing data analysing data deleting data archiving data for follow-on research own research publicly publishing data Research Scenarios and the General Data Protection Regulation: 8. Academic Research by International Public - Private Research Group & Third Parties Based on Generated Data from Data Subjects Combined With Existing Data and Commercial Data Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl August 24 2017 - informed consent - public interest generating new data data subjects adequate organisational and technical measures accessing data academic researcher academic researcher academic researcher sharing data for analysing, archiving and publishing purposes analysing data archiving data publishing data adequate organisational and technical measures accessing and collecting data existing data existing observed data online / sensor data non academic research partner non academic research partner licensed data non academic service provider non academic service provider principal investigator academia
  26. 26. storing data analysing data deleting data archiving data for follow-on research own research publicly publishing data principal investigator academia Research Scenarios and the General Data Protection Regulation: 9. Academic Big Data Research by International Public - Private Research Group & Third Parties Based on Generated Data from Data Subjects Combined With Existing Data and Commercial Data Marlon Domingus Erasmus University Rotterdam marlon.domingus@eur.nl August 24 2017 - informed consent - public interest generating new data data subjects adequate organisational and technical measures accessing data academic researcher academic researcher academic researcher sharing data for analysing, archiving and publishing purposes analysing data archiving data publishing data adequate organisational and technical measures accessing and collecting data existing data existing observed data online / sensor data non academic research partner non academic research partner licensed data non academic service provider non academic service provider HPC
  27. 27. juli 2017
  28. 28. Reflexive regulation • Situated norm (in context) • Multiple Actors • Learn Reprise: Two Models of Governance Focus Points: • Nature of the Data • Nature of the Consortium • Nature of the Dataflow • Appropriate Measures
  29. 29. Private, Shared and Public - Boundary Transitions 29 Source: Personal website Andrew Treloar. Online: http://andrew.treloar.net/research/diagrams/index.shtml
  30. 30. Cross Border Data Transfers Source: Prof. Christopher Kuner, International Transfers of Personal Data Post-GDPR. Brussels Privacy Hub, VUB Brussel, June 29 2017. 30
  31. 31. Privacy and the Internet of Things 31 Source: Internet of Things Architecture, pg 101, 102: http://iotforum.org/wp-content/uploads/2014/09/D1.5-20130715-VERYFINAL.pdf
  32. 32. Privacy and the Internet of Things Source: Internet of Things Architecture, pg 101, 102: http://iotforum.org/wp-content/uploads/2014/09/D1.5-20130715-VERYFINAL.pdf 32 The subject must be able to choose sharing or not sharing information with someone else; The subject must be able to fully control the mechanism used to ensure their privacy; The subject shall be able to decide for which purpose the information will be used; The subject shall be informed whenever information is used and by whom; During interactions between a subject and an IoT system, only strictly needed information shall be disclosed about the subject, and pseudonyms, secondary identity, or assertions (certified properties of the end-user) shall be used whenever possible; It shall not be possible to infer the subject‘s identity by aggregating/reasoning over information available at various sources; Information gained for a specific purpose shall not be used for another purpose. E.g., the bank issuing a credit card should not use a given client‘s purchase information (logged so to keep track of that client‘s account) to send him advertising on goods similar to his purchaces.
  33. 33. Privacy and the Internet of Things Source: Internet of Things Architecture, pg 101, 102: http://iotforum.org/wp-content/uploads/2014/09/D1.5-20130715-VERYFINAL.pdf 33 The subject must be able to choose sharing or not sharing information with someone else; The subject must be able to fully control the mechanism used to ensure their privacy; The subject shall be able to decide for which purpose the information will be used; The subject shall be informed whenever information is used and by whom; During interactions between a subject and an IoT system, only strictly needed information shall be disclosed about the subject, and pseudonyms, secondary identity, or assertions (certified properties of the end-user) shall be used whenever possible; It shall not be possible to infer the subject‘s identity by aggregating/reasoning over information available at various sources; Information gained for a specific purpose shall not be used for another purpose. E.g., the bank issuing a credit card should not use a given client‘s purchase information (logged so to keep track of that client‘s account) to send him advertising on goods similar to his purchaces.
  34. 34. What we don’t want; Data Breaches 2017: Source: Information is Beautiful: Data Breaches (public), bit.ly/bigdatabreaches 34
  35. 35. Next Steps: Privacy Maturity Model Source:LCRDM.https://www1.edugroepen.nl/sites/RDM_platform/RDM_Blog/Lists/Posts/Post.aspx?ID=12
  36. 36. Privacy Awareness: Infographics Source: EUR Research Matters website. Online: https://www.eur.nl/researchmatters/research_data_management/services/rdm_legal_services/ 36
  37. 37. Questions? drs. Marlon Domingus Research Services coordinator Community Research Data Management T +31 10 4088006 E researchsupport@eur.nl W https://www.eur.nl/researchmatters/research_data_management/ (services and templates) Stay in touch via: https://www.linkedin.com/in/domingus/ 37

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