# Calendar (tentative) Date | Topic | Presenters | Notes :----:|-------|:----------:|:-----: |

**Differential Privacy**

| | 9/4 | [Course welcome](../resources/slides/lecture-welcome.html)
**Reading:** [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) | JH | 9/6 | Basic private mechanisms
**Reading:** AFDP 3.2-4 | JH | 9/9 | Composition and closure properties
**Reading:** AFDP 3.5 | JH | Paper Signups 9/11 | What does differential privacy actually mean?
**Reading:** [Lunchtime for Differential Privacy](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) | JH | 9/13 | Differentially private machine learning
**Reading:** [*On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches*](https://arxiv.org/pdf/1708.08022)
**Reading:** [*Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data*](https://arxiv.org/pdf/1610.05755) | | |

**Adversarial Machine Learning**

| | 9/16 | Overview and basic concepts | JH | 9/18 | Adversarial examples
**Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf)
**Reading:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572)
**Reading:** [*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf) | | 9/20 | Data poisoning
**Reading:** [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389) | | 9/23 | Defenses and detection: challenges
**Reading:** [*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf)
**Reading:** [*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf) | JH | 9/25 | Certified defenses
**Reading:** [*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf)
**Reading:** [*Certified Defenses against Adversarial Examples*](https://arxiv.org/pdf/1801.09344) | | 9/27 | Adversarial training
**Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf) | | |

**Applied Cryptography**

| | 9/30 | Overview and basic constructions | JH | 10/2 | SMC for machine learning
**Reading:** [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329)
**Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212) | | 10/4 | Secure data collection at scale
**Reading:** [*Prio: Private, Robust, and Scalable Computation of Aggregate Statistics*](https://people.csail.mit.edu/henrycg/files/academic/papers/nsdi17prio.pdf) | | 10/7 | Verifiable computing
**Reading:** [*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268) | JH | 10/9 | Side channels and implementation issues
**Reading:** [*On Significance of the Least Significant Bits For Differential Privacy*](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.366.5957&rep=rep1&type=pdf) | | 10/11 | Model watermarking
**Reading:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf)
**Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) | | MS1 Due |

**Advanced Topic: Algorithmic Fairness**

| | 10/14 | Overview and basic notions
**Reading:** Chapter 2 from [Barocas, Hardt, and Narayanan](https://fairmlbook.org/demographic.html) | JH | 10/16 | Individual and group fairness
**Reading:** [*Fairness through Awarness*](https://arxiv.org/pdf/1104.3913)
**Reading:** [*Equality of Opportunity in Supervised Learning*](https://arxiv.org/pdf/1610.02413) | | 10/18 | Inherent tradeoffs
**Reading:** [*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807) | | 10/21 | Defining fairness: challenges
**Reading:** [*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104) | JH | 10/23 | Fairness in unsupervised learning
**Reading:** [*Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings*](https://arxiv.org/pdf/1607.06520)
**Reading:** [*Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints*](https://arxiv.org/pdf/1707.09457) | | 10/25 | Beyond observational measures
**Reading:** [*Avoiding Discrimination through Causal Reasoning*](https://arxiv.org/pdf/1706.02744)
**Reading:** [*Counterfactual Fairness*](https://arxiv.org/pdf/1703.06856) | | |

**Advanced Topic: PL and Verification**

| | 10/28 | Overview and basic notions | JH | 10/30 | Probabilistic programming languages
**Reading:** [*Probabilistic Programming*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/fose-icse2014.pdf) | | 11/1 | Automata learning and interpretability
**Reading:** [*Model Learning*](https://m-cacm.acm.org/magazines/2017/2/212445-model-learning/fulltext)
**Reading:** [*Interpreting Finite Automata for Sequential Data*](https://arxiv.org/pdf/1611.07100) | | 11/4 | Programming languages for differential privacy
**Reading:** [*Programming Language Techniques for Differential Privacy*](https://dl.acm.org/citation.cfm?id=2893591&dl=ACM&coll=DL) | JH | 11/6 | Verifying neural networks
**Reading:** [*AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation*](https://files.sri.inf.ethz.ch/website/papers/sp2018.pdf)
**Reading:** [*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf) | | 11/8 | Verifying probabilistic programs
**Reading:** [*Advances and Challenges of Probabilistic Model Checking*](https://www.prismmodelchecker.org/papers/allerton10.pdf)
**Reading:** [*A Program Logic for Union Bounds*](https://arxiv.org/pdf/1602.05681) | | MS2 Due |

**No Lectures: Work on Projects**

| | 12/11 (TBD) | Project Presentations | |