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GDPR Lightning Talk for CloudCamp London
Adrian Colyer
February 23, 2017
Transcript
17 slides
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General Data Protection Regulation “the law you’re probably not prepared
for” @adriancolyer -
May 2018 Icon made by EpicCoders from www.flaticon.com Fines €20M
or 4% of revenue, Whichever is the higher. -
Not just European companies. Anyone dealing with European Citizens, or
person data collected in Europe. Think customers, employees. - Safe Harbor ✗ - Privacy Shield ? - BCRs ! Icon made by FreePik from www.flaticon.com -
You can’t pass the buck! You’re still liable for the
actions of any 3rd party data processors selected. Tell me about all those SaaS services you use…? -
Spirit of the Law Make companies take protecting your personal
data seriously End “Surveillance Capitalism” Icons made by FreePik from www.flaticon.com -
Personal Data cookies IP addresses Article 29 WG Sensitive Personal
Data Identified or Identifiable -
Protection •“appropriate technical measures for data protection, taking into account
the state-of-the-art.” •“secure against loss, misuse, unauthorized access, disclosure, alteration, or destruction” JURISDICTION ENCRYPTION PREVENTION ACCESS CONTROL Icon made by FreePik from www.flaticon.com -
Collection & Consent •Minimum data •For a Specific Purpose •With
clear Explicit consent No dreaming up new uses outside of consent No Exploratory analysis? Consent metadata Provenance Joins & dataflows! Icons made by FreePik from www.flaticon.com -
Right to Access – you have one month Data Discovery
• Your own data centers • Public cloud • Third-party services • End-user devices • GOODS, Ground! Right to portability Icons made by FreePik and MadebyOliver from www.flaticon.com -
Profiling – here be dragons “Analyzing or predicting anything relating
to a person” • Explicit consent at point of data collection • Profiling logic explained • Envisaged consequences Use of sensitive data expressively forbidden. Icon made by FreePik from www.flaticon.com -
Right to an Explanation Easily understandable explanation of the result
of (E.g.,) your classifier. • Model selection implications? Must be able to override decision Icon made by EpicCoders from www.flaticon.com -
Explanations require provenance • And that’s a research problem •
Databases • Dataflows • Online models… Why should I trust you? Explaining the predictions of any classifier Explaining outputs in modern analytics -
Non-discrimination Icon made by OCHA from www.flaticon.com Harder than you
think. Uncertainty bias, Latent variables European Union regulations on algorithmic decision making and a “right to explanation” -
Right to be forgotten • Provenance requirements may favour event
sourcing, but the right to be forgotten doesn’t!