# Boosting and Differential Privacy

@article{Dwork2010BoostingAD,
title={Boosting and Differential Privacy},
author={Cynthia Dwork and Guy N. Rothblum and Salil P. Vadhan},
journal={2010 IEEE 51st Annual Symposium on Foundations of Computer Science},
year={2010},
pages={51-60}
}
Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved {\em privacy-preserving synopses} of an input database. These are data structures that yield, for a given set $\Q$ of queries over an input database, reasonably accurate estimates of the responses to every query in~$\Q$, even when the number of queries is much larger than the number of rows in the database. Given a {\em base synopsis generator} that takes a distribution on \$\Q… CONTINUE READING
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