Privacy-preserving data integration and sharing

Authors:
Chris Clifton;Murat Kantarcioǧlu;AnHai Doan;Gunther Schadow;Jaideep Vaidya;Ahmed Elmagarmid;Dan Suciu
Affiliations:
Purdue University;Purdue University;University of Illinois;The Regenstrief Institute for Healthcare;Rutgers University;Purdue University;The University of Washington
Venue:
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Year:
2004

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Abstract

Integrating data from multiple sources has been a longstanding challenge in the database community. Techniques such as privacy-preserving data mining promises privacy, but assume data has integration has been accomplished. Data integration methods are seriously hampered by inability to share the data to be integrated. This paper lays out a privacy framework for data integration. Challenges for data integration in the context of this framework are discussed, in the context of existing accomplishments in data integration. Many of these challenges are opportunities for the data mining community.