Polishing.

website/docs/format.md

@@ -1,12 +1,12 @@
-Lectures will be loosely organized around **four modules**: differential
+Lectures will be loosely organized around four **modules**: differential
 privacy, applied cryptography, language-based security, and adversarial machine
 learning. I will give most of the lectures for the first module (differential
-privacy). For the other modules, I will give an introductory lecture surveying
-the topic and background material. Then, each student will lead one lecture,
+privacy). For the other modules, I will give an overview lecture surveying the
+topic and background material. Then, each student will lead one lecture,
 presenting a paper and guiding the discussion.  
 
 This is a graduate seminar, so not all lectures are set in stone and there is
-considerable flexibility in the topics. If you are interested in something not
+considerable flexibility in the material. If you are interested in something not
 covered in the syllabus, please let me know!
 
 ## Readings and Homework
@@ -19,20 +19,20 @@ paper, (b) the primary contributions of the paper, and (c) how the authors solve
 the problem in some technical detail.  
 
 The topics we will be reading and thinking about are from the recent research
-literature---polished enough to be peer-reviewed and published, but not always
-completely refined.  Most research papers focus on a very narrow topic and are
-written for a very specific technical audience. It also doesn't help that
-computer science researchers are generally not the clearest writers (though
-there are certainly exceptions). These
+literature---peer-reviewed and published, but not always completely refined.
+Most research papers focus on a very narrow topic and are written for a very
+specific technical audience. It also doesn't help that computer science
+researchers are generally not the clearest writers, though there are certainly
+exceptions. These
 [notes](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) by
 Srinivasan Keshav may help you get more out of reading papers.
 
 To help you prepare for the class discussions, I will also send out a few
 questions at least 24 hours before every paper presentation. **Before** each
 lecture, you should send me brief answers---a short email is fine, no more than
-a few sentences per question. These questions are for your benefit---they are
-not meant to be very difficult or time-consuming and they will not be graded in
-detail.
+a few sentences per question. These questions will help you check that you have
+understood the papers---they are not meant to be very difficult or
+time-consuming and they will not be graded in detail.
 
 ## Course Project
 
@@ -54,9 +54,8 @@ Grades will be assigned as follows:
 
 By the end of this course, you should be able to...
 
-- Summarize the basic concepts in each of the four course modules: differential
-  privacy, applied cryptography, language-based security, and adversarial
-  machine learning.
+- Summarize the basic concepts in differential privacy, applied cryptography,
+  language-based security, and adversarial machine learning.
 - Use standard techniques from differential privacy to design privacy-preserving
   data analyses.
 - Grasp the high-level concepts from research literature on the main course

website/docs/syllabus.md

@@ -1,17 +1,14 @@
-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.  
+*Security and privacy* are rapidly emerging as critical research areas in
+computer science and beyond. Vulnerabilities in software are found and exploited
+almost everyday, with grave consequences. Personal data today is aggregated at
+large scales, increasing the risk of privacy violations or breaches. Finally,
+*machine-learning* (ML) algorithms are seeing real-world applications in
+critical sectors (e.g., health care, automation, and finance), but their
+behavior in the presence of malicious adversaries is poorly understood.
 
-This advanced topics class will tackle techniques related to all these themes.
-We will cover topics drawn from the following broad areas, depending on student
-interests:
+This advanced topics class will cover recent techniques from the frontiers of
+security and privacy research. Topics will be drawn from the following broad
+areas, depending on student interest:
 
 ### Differential Privacy
 - Basic properties and examples