Chapter

Trust, Privacy, and Security in Digital Business

Volume 3592 of the series Lecture Notes in Computer Science pp 246-255

Privacy Preserving Data Mining Services on the Web

  • Ayça Azgın HintoğluAffiliated withFaculty of Engineering and Natural Sciences, Tuzla, Sabancı University
  • , Yücel SaygınAffiliated withFaculty of Engineering and Natural Sciences, Tuzla, Sabancı University
  • , Salima BenbernouAffiliated withLIRIS – Lyon Research Center for Images and Intelligent Information Systems, Lyon 1 University
  • , Mohand Said HacidAffiliated withLIRIS – Lyon Research Center for Images and Intelligent Information Systems, Lyon 1 University

* Final gross prices may vary according to local VAT.

Get Access

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.