CS 229r: Mathematical Approaches to Data Privacy
Spring 2013 Schedule

Date

Topic

Reading/Reference

Homeworks

Jan 29

Introduction, Course Overview, Definition of Differential Privacy

Dwork-Roth, Ch. 1, Ch. 2 upto Def 4  

Jan 31

Randomized Response, Laplace Mechanism

Dwork-Roth, Sec. 3.2-3.3.0

 

Feb 5

Understanding the Definition of DP

Dwork-Roth, Ch. 2 and definition-notes.pdf  

Feb 7

Composition Theorems

Dwork-Roth, Sec 3.4 HW 1

Feb 12

Exponential Mechanism and Answering Many Queries via Synthetic Data

Dwork-Roth, Sec 3.3.1, Ch. 4 Intro, Sec. 4.1  

Feb 14

The Sparse Vector Technique

Dwork-Roth, Sec. 3.5  

Feb 19

Answering Many On-line Queries: Private Multiplicative Weights

Dwork-Roth, Sec. 4.2  

Feb 21

Attacks & Lower Bounds

Dwork-Roth, Ch. 8 (except Thm 113) and packing.pdf HW 1 due (Fri 2/22)
HW 2

Feb 26

Alternatives to Worst-Case Sensitivity

Dwork-Roth, 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: edge-level privacy

Karwa et al. "Private Analysis of Graph Structure"  

Mar 28

Graph analysis: node-level privacy

Blocki et al. "Differentially Private Data Analysis of Social Networks via Restricted Sensitivity", and Sec 3-4.2 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 1-3.1  

Apr 4

Differential Privacy in Computational Learning Theory, cont'd

Kasiviswanathan et al. "What Can We Learn Privately?" Sections 4-5.1 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 1-5 Project Proposals due (Fri 4/12)

Apr 16

Alternate Definitions: crowd-blending privacy

Gehrke et al. "Crowd-Blending Privacy"  

Apr 18

Alternate Definitions: the pufferfish framework

Kifer and Machanavajjhala "A Rigorous and Customizable Framework for Privacy"  

Apr 23

Traitor-Tracing and the Complexity of Differental Privacy

Ullman "Answering n2+o(1) Counting Queries with Differential Privacy is Hard" Sections 1-4
(If you're interested in how traitor-tracing 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 1-3, 5  

May 2

No class

  Project Papers due (Fri 5/3)

May 7

Project Presentations 9:30-11:30

   

May 9

Project Presentations 9:30-11:30