Privacy Preserving Data Mining Services on the Web

Abstract : Data mining research deals with extracting useful information from large collections of data. Since data mining is a complex process that requires expertise, it is beneficial to provide it as a service on the web. On the other hand, such use of data mining services combined with data collection efforts by private and government organizations leads to increased privacy concerns. In this work, we address the issue of preserving privacy while providing data mining services on the web and present an architecture for privacy preserving sharing of data mining models on the web. In the proposed architecture, data providers use APPEL for specifying their privacy preferences on data mining models, while data collectors use P3P policies for specifying their data-usage practices. Both parties use PMML as the standard for specifying data mining queries, constraints and models.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01586643
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Submitted on : Wednesday, September 13, 2017 - 9:36:52 AM
Last modification on : Tuesday, February 26, 2019 - 11:49:56 AM

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Ayca Azgin Hintoglu, Yucel Saygin, Salima Benbernou, Mohand-Said Hacid. Privacy Preserving Data Mining Services on the Web. Second International Conference on Trust, Privacy, and Security in Digital Business, TrustBus 2005, Aug 2005, Copenhagen, Denmark. pp.246-255, ⟨10.1007/11537878_25⟩. ⟨hal-01586643⟩

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