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Update syllabus.

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Justin Hsu 2018-06-03 20:20:59 -04:00
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@ -16,26 +16,27 @@ covered. Adversarial ML (what happens to ML algorithms in the
presence of adversaries?) will be also be discussed. List of some
topics that we will cover (obviously not complete) are given below.
Software Security
- Secure information flow
- Finding vulnerabilities
- Defensive measures and mitigations
Differential Privacy
- Basic mechanisms
- Local Differential Privacy
- Basic properties and examples
- Advanced mechanisms
- Local Differential Privacy
Cryptographic Techniques
- Zero-knowledge proofs
- Secure multi-party computation
- Verifiable computation
- Zero-knowledge proofs
- Secure multi-party computation
- Verifiable computation
Language-based Security
- Secure information flow
- Differential privacy
- Symbolic cryptography
Adversarial Machine Learning
- Training-time attacks
- Test-time attacks
- Model theft attacks
- Training-time attacks
- Test-time attacks
- Model-theft attacks
Grading will be based on three components:
- Reading research papers and writing reviews
- Homeworks
- Class project
- Reading research papers and writing reviews
- Homeworks
- Class project