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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
-
diff --git a/syllabus.md b/syllabus.md
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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