DIMACS Workshop on Recent Work on Differential Privacy across Computer Science

October 24 - 26, 2012
DIMACS Center, CoRE Building, Rutgers University

Organizers:

Aaron Roth, University of Pennsylvania, aaroth at cis.upenn.edu

Adam Smith, Pennsylvania State University, asmith at cse.psu.edu

Workshop Program:

Wednesday, October 24, 2012

Tutorials:

 8:30 -  9:00  Breakfast and Registration

 9:00 -  9:15  Opening Remarks 
               Aaron Roth and Adam Smith

 9:15 - 10:15  Tutorial on Differential Privacy in the Theory community
               Moritz Hardt

10:15 - 10:30  coffee/discussion

10:30 - 11:30  Tutorial on Differential Privacy in the Databases community  
               Gerome Miklau

11:45 -  1:00  Lunch

 1:00 -  1:15  Director's Welcome 
               Rebecca Wright, Director of DIMACS

 1:15 -  2:15  Tutorial on Differential Privacy in the Programming
               Languages community 
               Benjamin Pierce

 2:15 -  2:30  coffee/discussion

 2:30 -  3:30  Tutorial on Game Theory and Differential Privacy 
               Aaron Roth

 3:30 -  3:45  Coffee/discussion

 3:45 -  5:25  Talks, Session 1
               
               A Theory of Pricing Private Data
               Dan Suciu, University of Washington

               Is Privacy Compatible with Truthfulness?
               David Xiao, Laboratoire d'Informatique Algorithmique

               Kobbi Nissim, Ben-Gurion University - TBA

               Mechanism Design in Large Games: Incentives and Privacy
               Aaron Roth, University of Pennsylvania

 5:30 -  6:20  Talks, Session 2
               
               A Simple and Practical Algorithm for Differentially Private Data Release
               Katrina Ligett, Caltech
			   
 6:20          Dinner

Thursday, October 25, 2012

 8:30 -  9:00  Breakfast and Registration
 
 9:00 - 10:15  Talks, Session 3
               
               Cynthia Dwork, Microsoft Research - TBA

               The Johnson Lindenstrauss Lemma Itself Preserves Differential Privacy              
               Or Sheffet, Carnegie Mellon University
        
               The Geometry of (eps,delta) Differential Privacy
               Kunal Talwar, Microsoft Research

10:15 - 10:30  Coffee

10:30 - 11:45  Session 4

               iReduct: Differential Privacy with Reduced Relative Errors
               Michael Hay, Colgate University 
               
               Pufferfish: A Semantic Approach to the Privacy of Correlated Data              
               Ashwin Machanavajjhala, Duke University

               Data-driven concerns in Privacy
               Graham Cormode, AT&T Labs

11:45 -  1:00  Lunch

 1:00 -  2:15  Session 5

               Privacy, Fairness, and Social Norms
               Omer Reingold, Microsoft Research

               Crowd-Blending Privacy
               Rafael Pass, Cornell University  (Talk presented by Edward Lui)

               Friends Don't Let Friends Use Floating-Point Arithmetic              
               Ilya Mironov, Microsoft Research 

 2:15 -  2:30  Coffee

 2:30 -  3:45  Session 6

               The Price of Privacy: Sample Complexity Bounds
               for Differentially Private Classification             
               Kamalika Chaudhuri, UC San Diego 

              
               New Results in Differentially Private Empirical Risk Minimization
               Abhradeep Guha Thakurta, Penn State 

               The Power of Linear Reconstruction Attacks              
               Shiva Kasiviswanathan, GE Research 

 3:45 -  4:00  Coffee

 4:00 -  5:15  Session 7

               Answering n^{2+o(1)} Counting Queries with Differential
               Privacy is Hard
               Jon Ullman, Harvard University

               The Privacy of the Analyst and The Power of the State
               Salil Vadhan, Harvard University

 5:15 -  6:30  Student posters and presentations

 6:30          Dinner - on your own
 
 
Friday, October 26, 2012

 8:30 -  9:00  Breakfast and Registration
 
 9:00 - 10:15  Session 8

               Arvind Narayanan, Princeton University - TBA
               
               Guy Rothblum, Microsoft Research - TBA

               Zhiyi Huang, University of Pennsylvania - TBA

10:15 - 10:30  Coffee

10:30 - 11:45  Session 9

               Graph Analysis with Node-Level Differential Privacy
               Sofya Raskhodnikova, Penn State
			   
               Differentially Private Data Analysis of Social 
               Networks via Restricted Sensitivity
               Anupam Datta, Carnegie Mellon University

               A Workflow for Differentially-Private Graph Synthesis
               Sharon Goldberg, Boston University

11:45 -  1:00  Lunch

 1:00 -  2:15  Session 10

               Incoherence and privacy in spectral analysis of data
               Moritz Hardt, IBM Research 

               Near-Optimal Algorithms for Differentially-Private
               Principal Components
               Anand Sarwate, TTI 

               The Geometry of Differential Privacy: the Sparse Case
               Aleksandar Nikolov, Rutgers University

 2:15 -  2:30  Coffee

 2:30 -  3:45  Session 11
 
               Dan Kifer, Penn State - TBA
 
               Privacy-preserving Distributed Data Analysis
               Elaine Shi, University of Maryland 

               Differentially Private Join Queries over Distributed Databases
               Andreas Haeberlen, University of Pennsylvania

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