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Security and privacy are rapidly emerging as critical research areas in computer science and beyond. Vulnerabilities in software are found and exploited almost everyday, with grave consequences. Personal data today is aggregated at large scales, increasing the risk of privacy violations or breaches. Finally, machine-learning (ML) algorithms are seeing real-world applications in critical sectors (e.g., health care, automation, and finance), but their behavior in the presence of malicious adversaries is poorly understood.
This advanced topics class will cover recent techniques from the frontiers of security and privacy research. Topics will be drawn from the following broad areas, depending on student interest:
Differential Privacy
- Basic properties and examples
- Advanced mechanisms
- Local differential privacy
Adversarial Machine Learning
- Training-time attacks
- Test-time attacks
- Model-theft attacks
Cryptographic Techniques
- Zero-knowledge proofs
- Secure multi-party computation
- Verifiable computation