Unleashing Sensitive Datasets with Distributed Data Science

Unleashing Sensitive Datasets with Distributed Data Science

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[] Histograms with SQL queries on structured or unstructured data

31 of 38

31 of 38

[] Histograms with SQL queries on structured or unstructured data

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Unleashing Sensitive Datasets with Distributed Data Science

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  1. 1 [] Musical introduction to Blaise Thomson
  2. 2 [] From Intractable to Interactable: Unleashing Sensitive Datasets with Distributed Data Science
  3. 3 [] Outline
  4. 4 [] Introduction to Distributed Data Science
  5. 5 [] The context
  6. 6 [] What is going wrong?
  7. 7 [] Distributed Data Science
  8. 8 [] Sending Algorithms to Data
  9. 9 [] How Distributed Data Science solves the problem
  10. 10 [] Usage-based access control
  11. 11 [] Privacy protections
  12. 12 [] Dataset-level privacy-enhancing technologies
  13. 13 [] Collaboration-level privacy-enhancing technologies
  14. 14 [] Confidential Collaboration: Secure Aggregation and Federated Learning
  15. 15 [] Secure Aggregation
  16. 16 [] Federated Learning
  17. 17 [] An example experimental setup Retinal OCT Kaggle
  18. 18 [] An example: Error rates
  19. 19 [] Confidential Collaboration: Private Set Intersection
  20. 20 [] Private Set Intersection - Simple hashing
  21. 21 [] Private Set Intersection - more complex hashing
  22. 22 [] Other use cases
  23. 23 [] Configurations
  24. 24 [] Internal use within the company or group
  25. 25 [] Embedded: Single lead, leveraging partners' data
  26. 26 [] Data Resellers
  27. 27 [] Alliance - Consortium of data providers and data scientists
  28. 28 [] Summary
  29. 29 [] Sign up for the Bitfount Open Beta Launch at https://www.bitfount.com/!
  30. 30 [] Data Errors Obfuscation
  31. 31 [] Histograms with SQL queries on structured or unstructured data
  32. 32 [] Implicit bias detection or model failures
  33. 33 [] Theory vs Application
  34. 34 [] Skipping details approach
  35. 35 [] OpenMined PySyft
  36. 36 [] MLOops!
  37. 37 [] Siri products MLOops!
  38. 38 [] Wrap up

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