43 lines
1.5 KiB
Markdown
43 lines
1.5 KiB
Markdown
Security and Privacy are rapidly emerging as critical research areas.
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Vulnerabilities in software are found and exploited almost everyday
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and with increasingly serious consequences (e.g., the Equifax massive data
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breach). Moreover, our private data is increasingly at risk and thus
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techniques that enhance privacy of sensitive data (known as
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privacy-enhancing technologies (PETS)) are becoming increasingly
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important. Also, machine-learning (ML) is increasingly being utilized to
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make decisions in critical sectors (e.g., health care, automation, and
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finance). However, in deploying these algorithms presence of malicious
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adversaries is generally ignored.
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This advanced topics class will tackle techniques related to all these
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themes. We will investigate techniques to make software more secure.
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Techniques for ensuring privacy of sensitive data will also be
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covered. Adversarial ML (what happens to ML algorithms in the
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presence of adversaries?) will be also be discussed. List of some
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topics that we will cover (obviously not complete) are given below.
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Differential Privacy
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- Basic properties and examples
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- Advanced mechanisms
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- Local Differential Privacy
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Cryptographic Techniques
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- Zero-knowledge proofs
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- Secure multi-party computation
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- Verifiable computation
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Language-based Security
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- Secure information flow
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- Differential privacy
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- Symbolic cryptography
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Adversarial Machine Learning
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- Training-time attacks
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- Test-time attacks
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- Model-theft attacks
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Grading will be based on three components:
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- Reading research papers and writing reviews
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- Homeworks
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- Class project
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