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Justin Hsu 4 years ago
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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

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website/docs/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.
*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

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