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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
Description:
Explore privacy-preserving machine learning techniques in this 47-minute RSA Conference talk. Delve into the world of AI and privacy, learning how to combine these seemingly conflicting concepts without compromise. Discover emerging techniques that unlock AI's power while maintaining data privacy and confidentiality. Examine the higher computation and storage requirements of these methods, and understand recent research advancements in performance and usability. Investigate topics such as federated learning, trusted execution environments, and homomorphic encryption. Gain insights into real-world applications, including brain tumor segmentation challenges and the benefits of increased data availability. Analyze the mechanics, caveats, and architectures of various privacy-preserving approaches. Conclude with a discussion on future developments and potential applications in adverse settings.

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

RSA Conference
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