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