Mechanism Design via Differential Privacy

Author(s):  
Frank McSherry ◽  
Kunal Talwar

2015 ◽  
Vol 50 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Gilles Barthe ◽  
Marco Gaboardi ◽  
Emilio Jesús Gallego Arias ◽  
Justin Hsu ◽  
Aaron Roth ◽  
...  


2014 ◽  
Vol 104 (5) ◽  
pp. 431-435 ◽  
Author(s):  
Michael Kearns ◽  
Mallesh M. Pai ◽  
Aaron Roth ◽  
Jonathan Ullman

We study the design of mechanisms satisfying a novel desideratum: privacy. This requires the mechanism not reveal 'much' about any agent's type to other agents. We propose the notion of joint differential privacy: a variant of differential privacy used in the privacy literature. We show by construction that mechanisms satisfying our desiderata exist when there are a large number of players, and any player's action affects any other's payoff by at most a small amount. Our results imply that in large economies, privacy concerns of agents can be accommodated at no additional 'cost' to standard incentive concerns.





Author(s):  
Kobbi Nissim ◽  
Rann Smorodinsky ◽  
Moshe Tennenholtz




2015 ◽  
pp. 1-12
Author(s):  
Kobbi Nisim ◽  
David Xiao


2016 ◽  
pp. 1247-1256
Author(s):  
Kobbi Nisim ◽  
David Xiao


Sign in / Sign up

Export Citation Format

Share Document