Date 
Topic 
Reading/Reference 
Homeworks 
Jan 29 
Introduction, Course Overview, Definition of Differential Privacy

DworkRoth, Ch. 1, Ch. 2 upto Def 4  
Jan 31 
Randomized Response, Laplace Mechanism 
DworkRoth, Sec. 3.23.3.0 

Feb 5 
Understanding the Definition of DP 
DworkRoth, Ch. 2 and definitionnotes.pdf  
Feb 7 
Composition Theorems 
DworkRoth, Sec 3.4  HW 1 
Feb 12 
Exponential Mechanism and Answering Many Queries via Synthetic Data 
DworkRoth, Sec 3.3.1, Ch. 4 Intro, Sec. 4.1  
Feb 14 
The Sparse Vector Technique 
DworkRoth, Sec. 3.5  
Feb 19 
Answering Many Online Queries: Private Multiplicative Weights 
DworkRoth, Sec. 4.2  
Feb 21 
Attacks & Lower Bounds 
DworkRoth, Ch. 8 (except Thm 113) and packing.pdf  HW 1 due (Fri 2/22) HW 2 
Feb 26 
Alternatives to WorstCase Sensitivity 
DworkRoth, Ch. 7  
Feb 28 
Hardness of Generating Private Synthetic Data 
Ullman, Vadhan. "PCPs and the Hardness of Generating Private Synthetic Data." Sections 1,2, and 4.1 of syntheticdata.pdf  
Mar 5 
Class cancelled 

Mar 7 
Class cancelled 

Mar 12 
Finish Hardness of Synthetic Data 
Ullman, Vadhan. "PCPs and the Hardness of Generating Private Synthetic Data." Sections 3 and 4.2 of syntheticdata.pdf  Project topic ideas due (Wed 3/13) See project guidelines and project_ideas.pdf for more information 
Mar 14 
Faster Algorithms for Marginal Queries 
Thaler, Ullman, Vadhan. "Faster Algorithms for Privately Releasing Marginals." (Can skip Sections 4.2, 4.3) fastermarginals.pdf  HW 2 due (Fri 3/15) HW 3 
Mar 19 
Spring Break 

Mar 21 
Spring Break 

Mar 26 
Graph Analysis: edgelevel privacy 
Karwa et al. "Private Analysis of Graph Structure"  
Mar 28 
Graph analysis: nodelevel privacy 
Blocki et al. "Differentially Private Data Analysis of Social Networks via Restricted Sensitivity", and Sec 34.2 of Kasiviswanathan et al. "Analyzing Graphs with Node Differential Privacy"  
Apr 2 
Differential Privacy in Computational Learning Theory 
Kasiviswanathan et al. "What Can We Learn Privately?" Sections 13.1  
Apr 4 
Differential Privacy in Computational Learning Theory, cont'd 
Kasiviswanathan et al. "What Can We Learn Privately?" Sections 45.1  HW 3 due (Fri 4/5) 
Apr 9 
Differential Privacy & Mechanism Design: paying for private data 
Ghosh & Roth "Selling Privacy at Auction" (especially Section 2)  
Apr 11 
Differential Privacy & Mechanism Design: privacy for standard mechanism design problems 
Chen et al. "Truthful Mechanisms for Agents that Value Privacy" Sections 15  Project Proposals due (Fri 4/12) 
Apr 16 
Alternate Definitions: crowdblending privacy 
Gehrke et al. "CrowdBlending Privacy"  
Apr 18 
Alternate Definitions: the pufferfish framework 
Kifer and Machanavajjhala "A Rigorous and Customizable Framework for Privacy"  
Apr 23 
TraitorTracing and the Complexity of Differental Privacy 
Ullman "Answering n^{2+o(1)} Counting Queries with Differential Privacy is Hard" Sections 14 (If you're interested in how traitortracing schemes are constructed, feel free to read some or all of Section 5!) 

Apr 25 
Conclusions 
None  
Apr 30 
Differential Privacy in Computational Learning Theory, cont'd 
Beimel et al. "Characterizing the Sample Complexity of Private Learning," Sections 13, 5  
May 2 
No class 
Project Papers due (Fri 5/3)  
May 7 
Project Presentations 9:3011:30 

May 9 
Project Presentations 9:3011:30 