@conference {DworkRoVa10,
	title = {Boosting and Differential Privacy},
	booktitle = {Proceedings of the 51st Annual {IEEE} Symposium on Foundations of Computer Science (FOCS {\textquoteleft}10)},
	year = {2010},
	month = {23{\textendash}26 October},
	pages = {51{\textendash}60},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Las Vegas, NV},
	abstract = {Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved privacy-pre serving 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 base synopsis generator that takes a distribution on Q and produces a "weak" synopsis that yields "good" answers for a majority of the weight in Q, our Boosting for Queries algorithm obtains a synopsis that is good for all of Q. We ensure privacy for the rows of the database, but the boosting is performed on the queries. We also provide the first synopsis generators for arbitrary sets of arbitrary low-sensitivity queries, i.e., queries whose answers do not vary much under the addition or deletion of a single row. In the execution of our algorithm certain tasks, each incurring some privacy loss, are performed many times. To analyze the cumulative privacy loss, we obtain an O(ε2) bound on the expected privacy loss from a single e-differentially private mechanism. Combining this with evolution of confidence arguments from the literature, we get stronger bounds on the expected cumulative privacy loss due to multiple mechanisms, each of which provides e-differential privacy or one of its relaxations, and each of which operates on (potentially) different, adaptively chosen, databases.},
	url = {http://dx.doi.org/10.1109/FOCS.2010.12},
	author = {Cynthia Dwork and Guy Rothblum and Salil Vadhan}
}
@conference {McGregorMiPiReTaVa10,
	title = {The Limits of Two-Party Differential Privacy},
	booktitle = {Proceedings of the 51st Annual {IEEE} Symposium on Foundations of Computer Science (FOCS {\textquoteleft}10)},
	year = {2010},
	month = {23{\textendash}26 October},
	pages = {81{\textendash}90},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Las Vegas, NV},
	abstract = {We study differential privacy in a distributed setting where two parties would like to perform analysis of their joint data while preserving privacy for both datasets. Our results imply almost tight lower bounds on the accuracy of such data analyses, both for specific natural functions (such as Hamming distance) and in general. Our bounds expose a sharp contrast between the two-party setting and the simpler client-server setting (where privacy guarantees are one-sided). In addition, those bounds demonstrate a dramatic gap between the accuracy that can be obtained by differentially private data analysis versus the accuracy obtainable when privacy is relaxed to a computational variant of differential privacy. The first proof technique we develop demonstrates a connection between differential privacy and deterministic extraction from Santha-Vazirani sources. A second connection we expose indicates that the ability to approximate a function by a low-error differentially private protocol is strongly related to the ability to approximate it by a low communication protocol. (The connection goes in both directions).},
	url = {http://dx.doi.org/10.1109/FOCS.2010.14},
	author = {Andrew McGregor and Ilya Mironov and Toniann Pitassi and Omer Reingold and Kunal Talwar and Salil Vadhan}
}
