Course planning.
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Justin Hsu
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# Final Projects
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- Yue Gao and Fayi Zhang. *Theory and Optimization of Homomorphic Encryption*.
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- Yinglun Zhu. *Answering Evolving Sets of Queries*.
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- Yan Nan and Shimaa Ahmed. *Private Voice Transcription*.
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- Samuel Drews. *Verifying Decision Tree Stability*.
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- Madeleine Berner and Yaman Yu. *Evaluate Adversarial Machine Learning Attacks
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on Chinese Character Recognition*.
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- Kyrie Zhou and Meghana Moorthy Bhat. *Detecting Fake News with NLP*.
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- Hiba Nassereddine and Junxiong Huang. *Adversarial Machine Learning and Autonomous Vehicles*.
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- Zichuan Tian and Arjun Kashyap. *pyDiff: Differential Privacy as a Library*.
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- Zhiyi Chen and Yiqin Pan. *Detect Compromised Items from Data with Adversarial Attacks*.
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TBA
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# Project Details
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The goal of the course project is to dive more deeply into a particular topic.
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The project can be completed in **groups of two or three**. A good project could
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lead to some kind of publication. This project could take different forms:
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The project can be completed in **groups of three** (or in rare situations,
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groups of two). A good project could lead to some kind of publication. This
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project could take different forms:
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- **Conceptual**: Develop a new technique, extend an existing method, or explore
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a new application
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@ -36,8 +37,9 @@ should be clear what remains to be done.
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be done, along with reach goals to try if things go well.
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Besides the milestones, the main deliverable of the project will be a written
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final report, around **15-20 pages** in length. Reports should be written in a
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research paper style, covering the following areas in some reasonable order:
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final report, around **15-20 pages** in length (in some reasonable format).
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Reports should be written in a research paper style, covering the following
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areas in some reasonable order:
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- **Introduce** the problem and the motivation.
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- **Review** background and preliminary material.
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@ -46,7 +48,3 @@ research paper style, covering the following areas in some reasonable order:
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- **Survey** related work.
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At the end of the course, each group will give a brief project presentation.
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## Deadlines
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See [here](../schedule/deadlines.md).
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# Welcome to CS 763!
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This is a graduate-level course covering advanced topics in security and privacy
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in data science. We will focus on three core areas at the current research
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frontier: **differential privacy**, **adversarial machine learning**, and
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**applied cryptography** in machine learning. We will also cover selected
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advanced topics; this year, **algorithmic fairness** and **formal verification**
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for data science. This is primarily a project-based course, though there will
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also be paper presentations and small homework assignments.
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in data science. The field is eclectic, and so is this course. We will start
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with three core areas: **differential privacy**, **adversarial machine
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learning**, and **applied cryptography** in machine learning. Then, we will
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cover two advanced topic areas; this year, **algorithmic fairness** and **formal
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verification** for data science. This is primarily a project-based course,
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though there will also be paper presentations and small homework assignments.
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## Logistics
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- **Course**: CS 763, Fall 2019
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@ -49,10 +49,10 @@ time-consuming and they will not be graded in detail.
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### Homeworks
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After each of the first three core modules, we will assign a small homework
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assignment. These assignments are not weighed heavily---though they will be
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graded---but they are mostly for you to check that you have grasped the
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material.
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There will be three small homework assignments, one for each of the core
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modules. You will play with software implementations of the methods we cover in
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class. These assignments are not weighted heavily, though they will be lightly
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graded; the goal is to give you a chance to write some code.
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### Course Project
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- Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner.
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[*Local Differential Privacy for Evolving Data*](https://arxiv.org/abs/1802.07128).
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### Adversarial Machine Learning
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- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus.
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[*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf).
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ICLR 2014.
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- Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy.
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[*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572).
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ICLR 2015.
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- Nicholas Carlini and David Wagner.
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[*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf).
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S&P 2017.
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- Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song.
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[*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf).
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CVPR 2018.
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- Nicholas Carlini and David Wagner.
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[*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf).
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AISec 2017.
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- Jacob Steinhardt, Pang Wei Koh, and Percy Liang.
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[*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf).
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NIPS 2017.
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- Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu.
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[*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf).
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ICLR 2018.
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### Applied Cryptography
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- Benjamin Braun, Ariel J. Feldman, Zuocheng Ren, Srinath Setty, Andrew J. Blumberg, and Michael Walfish.
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[*Verifying Computations with State*](https://eprint.iacr.org/2013/356.pdf).
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[*Verifiable Differential Privacy*](https://www.cis.upenn.edu/~ahae/papers/verdp-eurosys2015.pdf).
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EUROSYS 2015.
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### Language-Based Security
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### Algorithmic Fairness
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- Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel.
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[*Fairness through Awarness*](https://arxiv.org/pdf/1104.3913).
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ITCS 2012.
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- Moritz Hardt, Eric Price, and Nathan Srebro.
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[*Equality of Opportunity in Supervised Learning*](https://arxiv.org/pdf/1610.02413).
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NIPS 2016.
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- Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai.
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[*Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings*](https://arxiv.org/pdf/1607.06520).
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NIPS 2016.
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- Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan.
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[*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807).
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ITCS 2017.
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- Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum.
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[*Multicalibration: Calibration for the (Computationally-Identifiable) Masses*](https://arxiv.org/pdf/1711.08513.pdf).
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ICML 2018.
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- Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu.
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[*Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness*](https://arxiv.org/pdf/1711.05144).
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ICML 2018.
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- Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach.
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[*A Reductions Approach to Fair Classification*](https://arxiv.org/pdf/1803.02453).
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ICML 2019.
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- Ben Hutchinson and Margaret Mitchell.
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[*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104).
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FAT\* 2019.
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### Programming Languages and Verification
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- Martín Abadi and Andrew D. Gordon.
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[*A Calculus for Cryptographic Protocols: The Spi Calculus*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/ic99spi.pdf).
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Information and Computation, 1999.
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[*Verification of a Practical Hardware Security Architecture Through Static Information Flow Analysis*](http://www.cse.psu.edu/~dbz5017/pub/asplos17.pdf).
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ASPLOS 2017.
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### Adversarial Machine Learning
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- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus.
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[*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf).
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ICLR 2014.
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- Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy.
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[*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572).
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ICLR 2015.
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- Nicholas Carlini and David Wagner.
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[*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf).
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S&P 2017.
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- Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song.
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[*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf).
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CVPR 2018.
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- Nicholas Carlini and David Wagner.
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[*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf).
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AISec 2017.
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- Jacob Steinhardt, Pang Wei Koh, and Percy Liang.
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[*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf).
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NIPS 2017.
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- Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu.
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[*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf).
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ICLR 2018.
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# Supplemental Material
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- Cynthia Dwork and Aaron Roth.
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[*Algorithmic Foundations of Data Privacy*](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf).
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## Differential Privacy
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## Core software
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- [TensorFlow](https://www.tensorflow.org/overview): Framework for writing,
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training, and testing neural networks. You should at least work through the
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[first tutorial](https://www.tensorflow.org/tutorials/keras/basic_classification).
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- [TensorFlow Privacy](https://github.com/tensorflow/privacy): Extensions to TF
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for differentially-private training.
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- [CleverHans](https://github.com/tensorflow/cleverhans): Extensions to TF for
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adversarial attacks and defenses on ML models.
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- [MPyC](https://github.com/lschoe/mpyc): Python libraries for Secure Multiparty
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Computation.
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## Other tools
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### Differential Privacy
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- [DFuzz](https://github.com/ejgallego/dfuzz)
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- [HOARe2](https://github.com/ejgallego/HOARe2)
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## Cryptography
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### Cryptography
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- [HELib](https://github.com/shaih/HElib)
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- [Obliv-C](https://oblivc.org/)
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- [ObliVM](http://oblivm.com/download.html)
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## Language-Based Security
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- [Jif](https://www.cs.cornell.edu/jif/)
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- [FlowCaml](https://opam.ocaml.org/packages/flowcaml/flowcaml.1.07/)
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## Adversarial Machine Learning
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- [CleverHans](https://github.com/tensorflow/cleverhans)
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The first key date is **September 16**. By this date, you should:
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The first key date is **September 9**. By this date, you should:
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- **Check in** with me briefly.
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- **Sign up** to present a paper.
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- **Choose** a project topic and form groups. This is not a firm commitment, but
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you should have an initial direction.
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- **Form project groups** of three.
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- **Brainstorm** project topics. Try to come up with **1-2 sentences**
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describing your initial direction. This is not a firm commitment---you can
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change your topic as you learn more.
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## Project Deadlines
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- Milestone 1: **October 7**
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- Milestone 1: **October 11**
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- Milestone 2: **November 8**
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- Final writeup and presentation: **December 11** (TBD)
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Date | Topic | Notes
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:----:|-------|:---------:
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| <center> <h4> **Differential Privacy** </h4> </center> |
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9/4 | [Course welcome](../resources/slides/lecture-welcome.html) <br> **Paper:** Keshav. [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf). |
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9/6 | |
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9/9 | |
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9/11 | |
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9/13 | |
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9/4 | [Course welcome](../resources/slides/lecture-welcome.html) <br> **Reading:** Keshav. [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf). | HW1 Out
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9/6 | Basic private mechanisms <br> **Reading:** AFDP 3.2-4 |
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9/9 | Composition and closure properties <br> **Reading:** AFDP 3.5 | Signups
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9/11 | What does differential privacy actually mean? <br> **Reading:** McSherry. [Lunchtime for Differential Privacy](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) |
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9/13 | Paper presentations | HW1 Due
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| <center> <h4> **Adversarial Machine Learning** </h4> </center> |
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9/16 | |
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9/20 | |
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9/23 | |
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9/25 | |
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9/27 | |
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9/16 | Overview and Basic attacks | HW2 Out
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9/18 | More attacks |
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9/20 | Paper presentations |
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9/23 | Defense: Adversarial training |
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9/25 | Defense: Certified defenses |
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9/27 | Paper presentations | HW2 Due
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| <center> <h4> **Applied Cryptography** </h4> </center> |
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9/30 | |
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10/9 | |
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10/11 | |
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9/30 | Overview and basic constructions | HW3 Out
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10/2 | Secure Multiparty Computation |
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10/4 | Paper presentations |
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10/7 | Homomorphic Encryption |
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10/9 | Oblivious computing and side channels |
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10/11 | Paper presentations | HW3 Due <br> MS1 Due
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| <center> <h4> **Advanced Topic: Algorithmic Fairness** </h4> </center> |
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10/14 | |
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10/16 | |
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10/18 | |
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10/21 | |
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10/23 | |
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10/25 | |
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10/14 | Overview and basic notions |
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10/16 | Individual and group fairness |
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10/18 | Paper presentations |
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10/21 | Repairing fairness |
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10/23 | Challenges in defining fairness |
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10/25 | Paper presentations |
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| <center> <h4> **Advanced Topic: PL and Verification** </h4> </center> |
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10/28 | |
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10/30 | |
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11/1 | |
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11/4 | |
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11/6 | |
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11/8 | |
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10/28 | Overview and basic notions |
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10/30 | Programming languages for differential privacy |
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11/1 | Paper presentations |
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11/4 | Probabilistic programming languages |
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11/6 | Verifying probabilistic programs |
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11/8 | Paper presentations | MS2 Due
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| <center> <h4> **No Lectures: Work on Projects** </h4> </center> |
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12/11 (TBD) | Project Presentations |
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@ -29,5 +29,5 @@ nav:
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- Related Courses: 'resources/related.md'
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- Assignments:
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- Presentations: 'assignments/presentations.md'
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- Project: 'assignments/project.md'
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- Projects: 'assignments/project.md'
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- Gallery: 'assignments/gallery.md'
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