Protect Privacy in a Data-Driven World - Privacy-Preserving Machine Learning

Protect Privacy in a Data-Driven World - Privacy-Preserving Machine Learning

RSA Conference via YouTube Direct link

Intro

1 of 27

1 of 27

Intro

Class Central Classrooms beta

YouTube playlists curated by Class Central.

Classroom Contents

Protect Privacy in a Data-Driven World - Privacy-Preserving Machine Learning

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Intro
  2. 2 DataDriven World
  3. 3 Current Approaches to Privacy
  4. 4 Trust
  5. 5 Machine Learning
  6. 6 PrivacyPreserving Machine Learning
  7. 7 Use Case
  8. 8 Outline
  9. 9 Unreasonable Effectiveness
  10. 10 Data Silo
  11. 11 Mechanics of Federated Learning
  12. 12 Caveats
  13. 13 Trusted Execution Environments
  14. 14 Federated Learning Architecture
  15. 15 Integrity and attestation features
  16. 16 Data science caveat
  17. 17 Brain tumor segmentation challenge
  18. 18 Benefits of more data
  19. 19 Homomorphic Encryption
  20. 20 Homomorphic Encryption Progress
  21. 21 Homomorphic Classification Progress
  22. 22 Conclusion
  23. 23 Homework
  24. 24 Questions
  25. 25 Explanation Ability Scheme
  26. 26 Federated Learning
  27. 27 Adverse Setting

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.