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New deals on data - Generating open knowledge ...

New deals on data - Generating open knowledge based on closed data

Talk hold at the "Blockchain for Science 2018" (Berlin).

Konrad Förstner

November 05, 2018
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  1. New deals on data – Generating open knowledge based on

    closed data Konrad U. Förstner ZB MED – Information Center for Life Sciences, Cologne, Germany & TH Köln, Cologne Germany November 5th, 2018, Blockchain for Science Con
  2. Disclaimer I have no to connection to any of the

    companies that I will be metioned here. I present my perspective as a bioinformatician and open science enthusiast. https://www.flickr.com/photos/redjar/113823307/ – CC-BY by flickr user redjar
  3. Open [data|source|*] should be the default in science. This is

    simply good scientific practice. https://www.flickr.com/photos/subcircle/500995147 – CC-BY by flickr user subcircle
  4. There are cases where privacy migh be a higher good

    than openess. Certain data should not be linked to individuals. https://commons.wikimedia.org/wiki/File:Masks_in_Venice.jpg CC-BY by Wikipedia user Rasevic
  5. Having access to the such data of a large popuplation

    would significantly help research and to extend our medicial knowledge. https://de.wikipedia.org/wiki/Datei:Crowd_at_Knebworth_House_-_Rolling_Stones_1976.jpg CC-BY by Wikimedia Commons Ibirapuera
  6. On the other hand the data can be misused for

    systematic discrimination due to political, ideological and commercial interests. https://www.flickr.com/photos/22394551@N03/2226095398 CC-BY by flickr user viZZZual.com
  7. We have moral dillemma. Protect individual rights or push the

    scientific progress. https://commons.wikimedia.org/wiki/File:Apothecary%27s_balance_with... CC-BY by Wikimedia Commons user Fæ
  8. Similar dilemmata from other research domains • Financial data of

    organisations • Energy consumption recording of devices • Location data of vehicles https://commons.wikimedia.org/wiki/File:Apothecary%27s_balance_with... CC-BY by Wikimedia Commons user Fæ
  9. Can we research based on black boxed data that is

    at least reproducible? https://commons.wikimedia.org/wiki/File:Eiserne_Truhe_Museum_Senftenberg.jpg PD
  10. Or can we at least use the data to generate

    hypthesis that then can be tested with complementary methods? https://commons.wikimedia.org/wiki/File:Eiserne_Truhe_Museum_Senftenberg.jpg PD
  11. Genomics England • Aims to hold 100,000 full genomes •

    Data processing in closed data centers • Only results leave the center via an ”airlock” https://de.wikipedia.org/wiki/Datei:Crowd_at_Knebworth_House_-_Rolling_Stones_1976.jpg CC-BY by Wikimedia Commons Ibirapuera
  12. Personal Health Train (PHT) • Data stations – (”FAIRports”) •

    Trains – Workflows that can work on the data provided to them https://de.wikipedia.org/wiki/Datei:Crowd_at_Knebworth_House_-_Rolling_Stones_1976.jpg CC-BY by Wikimedia Commons Ibirapuera
  13. (This slide was modified for online deposition - simply click

    on the link below; It is a news article that describes how 23andMe and other are selling genomic data to pharma industry.) https://www.businessinsider.de/dna-testing-delete-your-data-23andme-ancestry-2018-7
  14. Promises of blockchain-based, decentralized data marketplaces • owners have control

    over their data and can stay anonymous • standardisation of data • people can be incentivized to share the data • traceability (especially for pharmaceutical companies interesting) https://www.flickr.com/photos/katerha/4592429363 – CC-BY by flick user katerha
  15. Concepts of underlying solutions • Fully Homomorphic Encryption (FHE) •

    Multi-party Computation (MPC) • Trusted Execution Environment (TEE) like Intel SGX https://unsplash.com/@toddquackenbush?photo=IClZBVw5W5A - PD
  16. General purpose blockchain-based solutions • Ocean protocol • Enigma (secret

    contracts) • Ekiden protocol (Oasis Labs) • OpenMind https://unsplash.com/@toddquackenbush?photo=IClZBVw5W5A - PD
  17. Blockchain-based solutions for healthcare data • Nebula (by George Church)

    • Longenesis • Luna DNA • phrOS (Personal Health Record Operating System) • EncrypGen https://unsplash.com/@toddquackenbush?photo=IClZBVw5W5A - PD
  18. Currently lot of white papers available – nothing openly testable.

    https://www.flickr.com/photos/subcircle/500995147 – CC-BY by flickr user subcircle
  19. High risk – you won’t get your genome back once

    it leaked. https://www.flickr.com/photos/subcircle/500995147 – CC-BY by flickr user subcircle
  20. Implications for data owner/seller might be not clear – education

    needed. https://www.flickr.com/photos/subcircle/500995147 – CC-BY by flickr user subcircle
  21. Data stored off-chain = outsourcing of one important problem (suggestion

    like Dropbox metioned – IMO quite a bad idea) https://www.flickr.com/photos/subcircle/500995147 – CC-BY by flickr user subcircle
  22. How to avoid false statements in surveys to become interesting

    for data consumers? https://www.flickr.com/photos/subcircle/500995147 – CC-BY by flickr user subcircle
  23. Bottom line: Very promising, but a long and hard way

    to go. https://www.flickr.com/photos/subcircle/500995147 – CC-BY by flickr user subcircle
  24. What are your questions? konrad.foerstner.org / @konradfoerstner zbmed.de / @ZB_MED

    th-koeln.de / @th_koeln https://www.flickr.com/photos/nateone/3768979925/ – CC-BY by flick user nateone