1.5 KiB
Security and Privacy are rapidly emerging as critical research areas. Vulnerabilities in software are found and exploited almost everyday and with increasingly serious consequences (e.g., the Equifax massive data breach). Moreover, our private data is increasingly at risk and thus techniques that enhance privacy of sensitive data (known as privacy-enhancing technologies (PETS)) are becoming increasingly important. Also, machine-learning (ML) is increasingly being utilized to make decisions in critical sectors (e.g., health care, automation, and finance). However, in deploying these algorithms presence of malicious adversaries is generally ignored.
This advanced topics class will tackle techniques related to all these themes. We will investigate techniques to make software more secure. Techniques for ensuring privacy of sensitive data will also be 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.
Differential Privacy
- Basic properties and examples
- Advanced mechanisms
- Local Differential Privacy
Cryptographic Techniques
- 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
Grading will be based on three components:
- Reading research papers and writing reviews
- Homeworks
- Class project