Privacy preserving data mining services on the web

Authors:
Ayça Azgın Hintoğlu;Yücel Saygın;Salima Benbernou;Mohand Said Hacid
Affiliations:
Faculty of Engineering and Natural Sciences, Tuzla, Sabancı University, Istanbul, Turkey;Faculty of Engineering and Natural Sciences, Tuzla, Sabancı University, Istanbul, Turkey;LIRIS – Lyon Research Center for Images and Intelligent Information Systems, Lyon 1 University, Villeurbanne, France;LIRIS – Lyon Research Center for Images and Intelligent Information Systems, Lyon 1 University, Villeurbanne, France
Venue:
TrustBus'05 Proceedings of the Second international conference on Trust, Privacy, and Security in Digital Business
Year:
2005

Citing 5
Cited 1

Privacy-preserving data mining

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining models as services on the internet

ACM SIGKDD Explorations Newsletter
Executing SQL over encrypted data in the database-service-provider model

Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Foundations for an access control model for privacy preservation in multi-relational association rule mining

CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Association Rule Hiding

IEEE Transactions on Knowledge and Data Engineering

A Privacy-Protecting Business-Analytics Service for On-Line Transactions

International Journal of Electronic Commerce

Quantified Score

Hi-index	0.01

Visualization

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.