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Course welcome.

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Justin Hsu 2 years ago
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@ -1,7 +1,7 @@
---
author: Topics in Security and Privacy Technologies (CS 839)
author: Security and Privacy in Data Science (CS 763)
title: Course Welcome
date: September 05, 2018
date: September 04, 2019
---
# Security and Privacy
@ -41,76 +41,142 @@ date: September 05, 2018
- Automatically check security properties
- Search for attacks and vulnerabilities
## Our focus: four modules
## Five modules
1. Differential privacy
2. Applied cryptography
3. Language-based security
4. Adversarial machine learning
2. Adversarial machine learning
3. Applied cryptography
4. Algorithmic fairness
5. PL and verification
## This course is broad!
- Each module could be its own course
- We won't be able to go super deep
- You will probably get lost
- Our goal: broad survey of multiple areas
- Lightning tour, focus on high points
> Hope: find a few things that interest you
## This course is technical!
- Approach each topic from a rigorous point of view
- Parts of "data science" with **provable guarantees**
- This is not a "theory course", but...
. . .
![](images/there-will-be-math.png)
# Differential privacy
##
![](images/privacy.png)
## A mathematically solid definition of privacy
- Simple and clean formal property
- Satisfied by many algorithms
- Degrades gracefully under composition
# Applied crypto
## Computing in an untrusted world
- Proving you know something without revealing it
- Certifying that you did a computation correctly
- Computing on encrypted data, without decryption
- Computing joint answer without revealing your data
# Language-based security
# Adversarial machine learning
## Ensure security by construction
- Programming languages for security
- Compiler checks that programs are secure
- Information flow, privacy, cryptography, ...
##
# Adversarial machine learning
![](images/aml.jpg)
## Manipulating ML systems
- Crafting examples to fool ML systems
- Messing with training data
- Extracting training information
# Applied crypto
##
![](images/crypto-ml.png)
## Crypto in data science
- Learning models without raw access to private data
- Collecting analytics data privately, at scale
- Side channels and implementation issues
- Verifiable execution of ML models
- Other topics (e.g., model watermarking)
# Algorithmic fairness
##
![](images/fairness.png)
## When is a program "fair"?
- Individual and group fairness
- Inherent tradeoffs and challenges
- Fairness in unsupervised learning
- Fairness and causal inference
# PL and verification
##
![](images/pl-verif.png)
## Proving correctness
- Programming languages for security and privacy
- Interpreting neural networks and ML models
- Verifying properties of neural networks
- Verifying probabilistic programs
# Tedious course details
## Lecture schedule
- First ten weeks: **lectures MWF**
- Intensive lectures, get you up to speed
- M: I will present
- WF: You will present
- Last five weeks: **no lectures**
- Intensive work on projects
- I will be available to meet, one-on-one
> You must attend lectures and participate
## Class format
- Three components:
1. Paper presentations
2. Final project
3. Class participation
- Annoucement/schedule/materials: on [website](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs839/)
- Class mailing list: [compsci839-1-f18@lists.wisc.edu]()
2. Presentation summaries
3. Final project
- Announcement/schedule/materials: on [website](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/)
- Class mailing list: [compsci763-1-f19@lists.wisc.edu]()
## Paper presentations
- Sign up to lead a discussion on one paper
- Suggested topic, papers, and schedule on website
- Before each presentation:
- I will send out brief questions
- Please email me brief answers
> If you want advice, come talk to me!
- In pairs, lead a discussion on group of papers
- See website for [detailed instructions](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/assignments/presentations/jjj)
- See website for [schedule of topics](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/schedule/lectures/)
- One week **before** presentation: meet with me
- Come prepared with presentation materials
- Run through your outline, I will give feedback
## Presentation summaries
- In pairs, prepare written summary of another group
- See website for [detailed instructions](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/assignments/summaries/)
- See website for [schedule of topics](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/schedule/lectures/)
- One week **after** presentation: send me summary
- I will work with you to polish report
- Writeups will be shared with the class
## Final project
- Work individually or in pairs
- Project details and suggestions on website
- In groups of three (or very rarely two)
- See website for [project details](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/assignments/project/)
- Key dates:
- **September 19**: Pick groups and topic
- **October 15**: Milestone 1
- **November 14**: Milestone 2
- **September 9**: Form groups, pick topic
- **October 11**: Milestone 1
- **November 8**: Milestone 2
- **End of class**: Final writeups and presentations
> If you want advice, come talk to me!
## Todos for you
0. Complete the course survey
1. Check out the course website
2. Think about what paper you want to present
3. Brainstorm project topics
0. Complete the [course survey](https://forms.gle/NvYx3BM7HVkuzYdG6)
1. Explore the [course website](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/)
2. Think about which lecture you want to present
3. Think about which lecture you want to summarize
4. Brainstorm project topics
# Defining privacy

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