| Week | Date | Topic | Reading | Deadlines |
|---|---|---|---|---|
| 1 | 8/25/2021 | Introduction to the course (lecture slides) | ||
| MODULE 1: Intro to Privacy | ||||
| 8/27/2021 | Privacy Attacks Practicum (lecture slides) Link to exercise | Machanavajjhala, Kifer, "Designing Statistical Privacy For Your Data", CACM 2015 (PDF) Dinur, Nissim, "Revealing Information while Preserving Privacy", PODS 2003 (PDF) | ||
| 2 | 9/1/2021 | Differential Privacy (lecture slides) | Dwork, Roth, "Algorithmic Foundations of Differential Privacy", Foundations and Trends 9(3-4), 2014 (PDF) | |
| 9/3/2021 | DP Primitives (lecture slides) | Privacy Textbook, Chapter 3 (till 3.1-3.4) | ||
| 3 | 9/8/2021 | Composition Theorems (lecture slides) | Privacy Textbook, Chapter 3.4 Optional Reading: Mironov, "Renyi DP" (PDF) | |
| 9/10/2021 | Optimizing accuracy of DP algorithms (lecture slides) | DPComp.org Hay, Rastogi, Miklau, Suciu, "Boosting the Accuracy of Differentially Private Histograms Through Consistency", PVLDB 3(1) 2010 (PDF) | HW1 (files) | |
| MODULE 2: Intro to Fairness | ||||
| 4 | 9/15/2021 | Fairness Practicum lecture slides Link to exercise | Angwin, Julia, "Machine Bias", Propublica 2016 (Article) | |
| 9/17/2021 | Fairness in ML-1 (lecture slides) | Dwork, Cynthia, "Fairness Through Awareness", ITCS 2012 (PDF) Feldman, Michael, "Certifying and Removing Disparate Impact" KDD 2015 (PDF) | ||
| 5 | 9/22/2021 | Fairness in ML-2 (lecture slides) | Hardt, Moritz, "Equality of Opportunity in Supervised Learning", NIPS 2016 (PDF) | |
| 9/24/2021 | Fairness in Resource Allocation (lecture slides) | Fain, Brandon "The Core of the Participatory Budgeting Problem" WINE 2017 (PDF) Privacy Textbook, Chapter 3.4 "Exponential Mechanism" | HW1 due @ 6PM HW2 (files) assigned |
|
| MODULE 3: Privacy (Paper reading) | Paper Reading Guidelines PDF | |||
| 6 | 9/29/2021 | Data Adaptive Algorithms Student slides (PDF) | Student Presenter: Xinyi Pan Required Reading: Li et al "A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy" (PDF) | Due date to choose project team/idea |
| 10/1/2021 | Private Machine Learning Student slides ((PDF)) Instructor slides (PDF) | Student Presenter: Zonghao Huang Required Reading: Papernot et al, "Private Aggregation of Teacher Ensembles (PATE)", (PDF) (Blog) Optional Reading: Abadi et al, "Differentially Private Deep Learning", ACM CCS 2016 (PDF) | ||
| 8 | 10/6/2021 | Synthetic Data (slides) | Guest Lecture: Yuchao Tao Required Reading: Hardt et al "A simple and practical algorithm for differentially private data release" | |
| 7 | 10/8/2021 | Differential Privacy and SQL queries (slides) | Presenter: Ashwin Required Reading: Kotsogiannis et al "PrivateSQL" (PDF) | HW2 due @ 6 PM |
| 10/13/2021 | Differential Privacy and Correlations | Student Presenter: Shweta Patwa Required Reading: Kifer, Machanavajjhala, "No Free Lunch in Differential Privacy", SIGMOD 2011 (PDF) Optional Reading: Kifer, Machanavajjhala, "Pufferfish", ACM TODS 2014 (PDF) | ||
| 10/15/2021 | PROJECT WORK | Project proposal due @ 6:00 PM | ||
| 9 | 10/20/2021 | Local Differential Privacy (slides) | Guest Lecture: Chenghong Wang Required Reading: Erlingsson et al, "RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response", ACM CCS 2014 (PDF) | |
| 10/22/2021 | Composing Differential Privacy with Crypto primitives (slides) | Guest Lecture: Johes Bater Required Reading: Wagh et al, "DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications"(PDF) | ||
| 10 | 10/27/2021 | Federating Learning (slides) | Student Presenter: Chad Kalil Required Reading: Section 4 of Kairouz et al "Advances and Open Problems in Federated Learning" PDF Optional Reading: McMahan et al, "Communication-Efficient Learning of Deep Networks from Decentralized Data" PDF | |
| MODULE 4: Fairness (Paper reading) | ||||
| 10/29/2021 | Trade-offs in Fairness (slides) | Student Presenter: Oscar Quintero Required Reading: Inherent Trade-Offs in the Fair Determination of Risk Scores by Kleinberg, Mullainathan, and Raghavan; appeared in ITCS 2017. (PDF) | ||
| 11 | 11/3/2021 | PROJECT WORK | ||
| 11/5/2021 | Causal inference and Fairness | Student Presenter: Jack Goffinet Required Reading: Salimi et al, "Causal Database Repair for Algorithmic Fairness", (PDF) | Midterm report due @ 11:59 AM | |
| 12 | 11/10/2021 | (Un)fairness in feature generation | Student Presenter: Yalu Cai Required Reading: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings by Bolukbasi et. al; appeared in NIPS 2016. (PDF) | |
| 11/12/2021 | PROJECT WORK | |||
| 13 | 11/17/2021 | Privacy vs Fairness | Guest Lecture: David Pujol Required Reading: Pujol et al, "Fair Decision Making using Privacy-Protected Data" (PDF) | |
| 11/19/2021 | Resource allocation of multiple resources | Student Presenter: Shao-Heng Ko Required Reading: Dominant Resource Fairness: fair allocation of multiple resource types by Ghodsi et. al; appeared in NSDI 2011. (PDF) | ||
| FINAL | 12/13/2021 | Final Report Due Date | Final Report due @ 11:59 AM |
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