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Preserving Privacy in Data Mining via Importance Weighting

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Privacy and Security Issues in Data Mining and Machine Learning (PSDML 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6549))

Abstract

This paper presents a fundamentally new approach to allowing learning algorithms to be applied to a dataset, while still keeping the records in the dataset confidential. Let D be the set of records to be kept private, and let E be a fixed set of records from a similar domain that is already public. The idea is to compute and publish a weight w(x) for each record x in E that measures how representative it is of the records in D. Data mining on E using these importance weights is then approximately equivalent to data mining directly on D. The dataset D is used by its owner to compute the weights, but not revealed in any other way.

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Elkan, C. (2011). Preserving Privacy in Data Mining via Importance Weighting. In: Dimitrakakis, C., Gkoulalas-Divanis, A., Mitrokotsa, A., Verykios, V.S., Saygin, Y. (eds) Privacy and Security Issues in Data Mining and Machine Learning. PSDML 2010. Lecture Notes in Computer Science(), vol 6549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19896-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-19896-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19895-3

  • Online ISBN: 978-3-642-19896-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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