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schedule.md
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CS 839: Advanced Topics in Security and Privacy
Fall semester instruction begins: Sep 5, 2018 (W)
Thanksgiving recess: Nov 22-25, 2018
Last class day: Dec 12, 2018 (W)
Exams: Dec 14 (F)-Dec 20 (R), 2018
Length: 14.5 weeks, 29 classes plus exams
Lectures: MW 4:00-5:15 in CS 1325
# Lecture 01 (9/5): Course intro and Privacy overview
# Lecture 02 (9/10): DP Definition and Basic Mechanisms
# Lecture 03 (9/12): DP Implications
# Lecture 04 (9/17): DP Composition and closure properties
# Lecture 05 (9/19): DP Exponential mechanism
# Lecture 06 (9/24): DP Streaming counters
# Lecture 07 (9/26): DP Advanced mechanisms: RNM
# Lecture 08 (10/1): DP Advanced mechanisms: SVT
# Lecture 09 (10/3): DP Advanced mechanisms: PMW
# Lecture 10 (10/8): DP Local Model (Theory)
# Lecture 11 (10/10): DP Local Model (Practice)
# Lecture 12 (10/15): Crypto Overview and basics
# Lecture 13 (10/17): Crypto Zero-knowledge proofs
# Lecture 14 (10/22): Crypto OT and SMC
# Lecture 15 (10/24): Crypto OT and SMC
# Lecture 16 (10/29): Crypto FHE and verifiable computing
# Lecture 17 (10/31): Crypto FHE and verifiable computing
# Lecture 18 (11/5): LangSec Overview and basics
# Lecture 19 (11/7): LangSec Secure Information Flow
# Lecture 20 (11/12): LangSec Secure Information Flow
# Lecture 21 (11/14): LangSec Differential Privacy
# Lecture 22 (11/19): LangSec Differential Privacy
# Lecture 23 (11/21): LangSec Symbolic Crypto
# Lecture 24 (11/26): AML Overview and basics
# Lecture 25 (11/28): AML Adversarial Examples
# Lecture 26 (12/3): AML Adversarial Examples
# Lecture 27 (12/5): AML Training-time attacks
# Lecture 28 (12/10): AML Training-time attacks
# Lecture 29 (12/12): AML Model-theft attacks

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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