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» Differentially Private Approximation Algorithms
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STOC
2007
ACM
112views Algorithms» more  STOC 2007»
14 years 8 months ago
Smooth sensitivity and sampling in private data analysis
We introduce a new, generic framework for private data analysis. The goal of private data analysis is to release aggregate information about a data set while protecting the privac...
Kobbi Nissim, Sofya Raskhodnikova, Adam Smith
STOC
2009
ACM
167views Algorithms» more  STOC 2009»
14 years 8 months ago
Universally utility-maximizing privacy mechanisms
A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Publishing fully accurate information ...
Arpita Ghosh, Tim Roughgarden, Mukund Sundararajan
ICFP
2010
ACM
13 years 8 months ago
Distance makes the types grow stronger: a calculus for differential privacy
We want assurances that sensitive information will not be disclosed when aggregate data derived from a database is published. Differential privacy offers a strong statistical guar...
Jason Reed, Benjamin C. Pierce
IEEECGIV
2006
IEEE
14 years 1 months ago
Effects of Different Order PDEs on Blending Surfaces
In this paper, we introduce second order and mixed order partial differential equations (PDEs) for surface blending and present an approximate algorithm for the resolution of the ...
Lihua You, Jian J. Zhang
SPAA
1998
ACM
13 years 11 months ago
Analyses of Load Stealing Models Based on Differential Equations
In this paper we develop models for and analyze several randomized work stealing algorithms in a dynamic setting. Our models represent the limiting behavior of systems as the numb...
Michael Mitzenmacher