Rearrange.

description.md

@@ -1,41 +0,0 @@
-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.
-
-Software Security
-  - Secure information flow
-  - Finding vulnerabilities
-  - Defensive measures and mitigations
-
-Differential Privacy
-  - Basic mechanisms
-  - Local Differential Privacy
-
-Cryptographic Techniques
-  - Zero-knowledge proofs
-  - Secure multi-party computation
-  - Verifiable computation
-
-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

previous.md

@@ -1,37 +0,0 @@
-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.

syllabus.md

@@ -0,0 +1,41 @@
+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.
+
+Software Security
+  - Secure information flow
+  - Finding vulnerabilities
+  - Defensive measures and mitigations
+
+Differential Privacy
+  - Basic mechanisms
+  - Local Differential Privacy
+
+Cryptographic Techniques
+  - Zero-knowledge proofs
+  - Secure multi-party computation
+  - Verifiable computation
+
+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