1.5 KiB
Security and Privacy are emerging as very important research areas. Vulnerabilities in software are found and exploited almost everyday and with disastrous 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.
Software Security:
- Information flow
- Techniques for finding vulnerabilities in software
- Defense techniques (e.g., control-flow integrity)
Privacy:
- Differential Privacy
- Zero-knowledge proofs
- Secure multi-party computation
Adversarial ML:
- Training-time attacks
- Test-time attacks
- Model Theft attacks
Grading: There are three components that relate to grading:
- Reading research papers and writing reviews.
- Few homeworks.
- Class project.