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Tutorial

1. Federated Learning
2. Model Inversion
3. Label Leakage
- 3.1. Split Learning and Label Leakage
4. Membership Inference
- 4.1. Memership Inference
5. Poisoning Attack
- 5.1. Poisoning Attack against Federated Learning
- 5.2. Poisoning Attack against SVM
6. Backdoor Attack
- 6.1. Backdoor Attack against Federated Learning
7. Evasion Attack
8. Differential Privacy
9. K-anonymity
- 9.1. K-anonymity
10. Debugging
- 10.1. Neuron Coverage
- 10.2. Model Assertions
11. Homomorphic Encryption
- 11.1. Paillier Encryption

API Docs

1. aijack.attack package
2. aijack.defense package
3. aijack.collaborative package
4. aijack.utils package

Developer Docs

1. Contribution Guide

.rst

aijack.defense.dp package

Contents

2.1.3.1. Subpackages
2.1.3.2. Module contents

2.1.3. aijack.defense.dp package#

2.1.3.1. Subpackages#

2.1.3.1.1. aijack.defense.dp.manager package

2.1.3.2. Module contents#

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2.1.2.1.2. aijack.defense.debugging.neuroncoverage package

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2.1.3.1.1. aijack.defense.dp.manager package

Contents

2.1.3.1. Subpackages
2.1.3.2. Module contents

By Hideaki Takahashi

© Copyright 2023, Hideaki Takahashi.