From 28091ba43e281604edfd659b19cc8b52f348a412 Mon Sep 17 00:00:00 2001 From: Justin Hsu Date: Sun, 3 Jun 2018 19:02:46 -0400 Subject: [PATCH] Rearrange. --- description.md | 41 ----------------------------------------- previous.md | 37 ------------------------------------- syllabus.md | 41 +++++++++++++++++++++++++++++++++++++++++ 3 files changed, 41 insertions(+), 78 deletions(-) delete mode 100644 description.md delete mode 100644 previous.md diff --git a/description.md b/description.md deleted file mode 100644 index 9233173..0000000 --- a/description.md +++ /dev/null @@ -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 diff --git a/previous.md b/previous.md deleted file mode 100644 index 3657962..0000000 --- a/previous.md +++ /dev/null @@ -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. diff --git a/syllabus.md b/syllabus.md index e69de29..9233173 100644 --- a/syllabus.md +++ b/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