2010 DataMiningwithDifferentialPriva

• (Friedman et al., 2010) ⇒ Arik Friedman, and Assaf Schuster. (2010). “Data Mining with Differential Privacy.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835868

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• http://scholar.google.com/scholar?q=%22Data+mining+with+differential+privacy%22+2010
• http://portal.acm.org/citation.cfm?id=1835868&preflayout=flat#citedby

Quotes

Abstract

We consider the problem of data mining with formal privacy guarantees, given a data access interface based on the differential privacy framework. Differential privacy requires that computations be insensitive to changes in any particular individual's record, thereby restricting data leaks through the results. The privacy preserving interface ensures unconditionally safe access to the data and does not require from the data miner any expertise in privacy. However, as we show in the paper, a naive utilization of the interface to construct privacy preserving data mining algorithms could lead to inferior data mining results. We address this problem by considering the privacy and the algorithmic requirements simultaneously, focusing on decision tree induction as a sample application. The privacy mechanism has a profound effect on the performance of the methods chosen by the data miner. We demonstrate that this choice could make the difference between an accurate classifier and a completely useless one. Moreover, an improved algorithm can achieve the same level of accuracy and privacy as the naive implementation but with an order of magnitude fewer learning samples.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 DataMiningwithDifferentialPriva	Assaf Schuster Arik Friedman			Data Mining with Differential Privacy		KDD-2010 Proceedings		10.1145/1835804.1835868		2010