GDPR: Threat or Opportunity?
GDPR: threat or opportunity? In May 2018, the European Union’s General Data Protection Regulation (https://en.wikipedia.org/wiki/General_Data_Protection_Regulation) (GDPR) will take effect. Companies that do not comply might be fined 20M, or 4% of their annual global turnover, whichever is greater. Despite the evident threat, GDPR is also a huge opportunity to rethink how your business works, and turn that threat into an opportunity. In this talk, we will show how GRAKN.AI (http://grakn.ai/) – a knowledge base – provides everything you need to turn your centralised record of users, as required by GDPR, and use it to provide value to your users. Adding them to a knowledge base, as well as your content or product, can open many new perspectives. =================================== About Samuel Pouyt (https://www.linkedin.com/in/samuel-pouyt/), Web Specialist, European Respiratory Society (https://www.ersnet.org/) Samuel Pouyt (https://www.linkedin.com/in/samuel-pouyt/) is a software architect and full stack developer at the European Respiratory Society (https://www.ersnet.org/) (ERS). His curriculum reflects his interests and curiosity. He started in mechanical automation, then obtained a Master of Arts in English Linguistics, American Literature, Russian and Musicology, while working as a developer. He is currently working on Natural Language Processing solutions and recommendation engines for the ERS while taking care of numerous websites and applications.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to GDPR: Threat or Opportunity?
Similar to GDPR: Threat or Opportunity? (20)
More from Vaticle
More from Vaticle (20)
Recently uploaded
Recently uploaded (20)
GDPR: Threat or Opportunity?
- 5. CONTEXT – EUROPEAN RESPIRATORY SOCIETY 127 C o u n t r i e s 25 000 D e l e g a t e s 70 S c i e n t i f i c S e s s i o n s 4000 O r i g i n a l a b s t r a c t s 50 E d u c a t i o n a l s e e s i o n s 180 I n t e r n a t i o n a l e x h i b i t o r s 40 000 M e m b e r s 120 000 N e w s l e t t e r
- 6. CONTEXT Who I am? • Not a lawyer... • Full Stack Dev - Software Architect • ERS 01 General Data Protection Regulation 02 European Law • But... 03 May 2018 But we are supposed to know about it since 2016 04
- 7. PRIVACY Not optional anymore. By design By default 01 Demonstrable • Conception • Certification 02 Older software • Documented process 03
- 9. ” “Any information related to a natural person or ‘Data Subject’, that can be used to directly or indirectly identify the person. It can be anything from a name, a photo, an email address, bank details, posts on social networking websites, medical information, or a computer IP address. http://www.eugdpr.org/gdpr-faqs.html
- 10. PRIVACY – DATA SUBJECT Anything and everything Right to access 01 Consent • Easy to give • Easy to withdraw 02
- 12. PRIVACY – DATA SUBJECT Anything and everything Right to access 01 Consent • Easy to give • Easy to withdraw 02 Right to be forgotten 03
- 13. PRIVACY – DATA SUBJECT Anything and everything Right to access 01 Consent • Easy to give • Easy to withdraw 02 Right to be forgotten 03 Data Portability • exactitude of data 04
- 14. PRIVACY – DATA SUBJECT Anything and everything Right to access 01 Consent • Easy to give • Easy to withdraw 02 Right to be forgotten 03 Data Portability • exactitude of data 04 Re- identification • Read indirect :( 05
- 15. ” “ De Montjoye and colleagues examined three months of credit card transactions for 1.1 million people, all of which had been scrubbed of any [personally identifiable information]. Still, 90% of the time he managed to identify individuals in the dataset using the date and location of just four of their transactions. By adding knowledge of the price of the transactions, he increased “reidentification” (the academic term for spotting an individual in anonymized data) to 94%. Additionally, women were easier to reidentify than men, and reidentification ability increased with income of the consumer. https://hbr.org/2015/02/theres-no-such-thing-as-anonymous-data
- 16. ” “ Latanya Sweeney found that 87 percent of the population in the United States, 216 million of 248 million, could likely be uniquely identified by their five- digit ZIP code, combined with their gender and date of birth. https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/
- 21. THREAT What we know • Fines • When • The clock is ticking… 01 What we don’t know • How it will be applied • How far • Good will/Due diligence 02
- 23. OPPORTUNITY (ONE AMONG MANY) Data opportunity • We need to know everything about the data we hold about a user or data subject • Tracking system 01 Mix everything • Expand the tracking system • Mix content into it • Find a way to be GDPR compliant 02
- 24. GDPR + =
- 26. ” “ THE DATABASE FOR AI Grakn is a hyper-relational database for knowledge engineering. Rooted in Knowledge Representation and Automated Reasoning, Grakn provides the knowledge base foundation for intelligent/cognitive systems.
- 27. ARCHITECTURE
- 28. SCHEMA
- 29. SCHEMA property sub entity is-abstract has value plays owned plays demand plays authorizer plays exported plays imported plays revoker plays withdrawer; last-name sub property; first-name sub property; email sub property; match $a isa property; $p isa person, has identifier 123456; (exporter:$x, exported:$a, exported-to:$z); (owned:$a, owner:$p); get; address sub property has value; city sub address; zip sub address; street1 sub address; street2 sub address; street3 sub address;
- 30. SCHEMA person sub entity has timestamp has type has identifier plays identified plays imported plays importer plays exported plays exporter plays owner; match $p isa person; $e isa email, has value "member@test.com"; ($e, $p) isa belongs; get $p; match $p isa person has identifier 1;get;
- 31. SCHEMA – HOW TO EXTEND IT AND BE GDPR COMPLIANT? anonymous sub entity has timestamp plays incognito;
- 32. SCHEMA – HOW TO EXTEND IT AND BE GDPR COMPLIANT? anonymous: V123456 anonymous: V123457 article: V1 article: V2 article: V3 article: V4 article: V5 topic: V14 topic: V11 topic: V15 topic: V12 topic: V16 topic: V13 topic: V17
- 33. SCHEMA – AUTHORIZATION authorization sub entity has name has description has timestamp has expiration-date plays needed plays requisite plays revoked plays withdrawn;
- 35. INFERENCE define is-authorized sub rule, when { (demand: $a, needed: $b) isa needs; (requisite: $b, requester: $c ) isa requires; } then { (authorizer: $a, authorized: $c) isa authorizes; }; is-revoked sub rule, when { (revoker:$a, revoked:$b) isa revoke; (requisite:$b, requester: $c) isa requires; } then { (withdrawer:$a, withdrawn:$c) isa withdraws; };
- 37. DASHBOARD