Privacy-preserving data integration and sharing

Chris Clifton; Murat Kantarcioǧlu; An Hai Doan; Gunther Schadow; Jaideep Vaidya; Ahmed Elmagarmid; Dan Suciu

doi:10.1145/1008694.1008698

Privacy-preserving data integration and sharing

Chris Clifton^*, Murat Kantarcioǧlu, An Hai Doan, Gunther Schadow, Jaideep Vaidya, Ahmed Elmagarmid, Dan Suciu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

148 Citations (Scopus)

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.

Original language	English
Title of host publication	Workshop Proceedings - The 9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04
Pages	19-26
Number of pages	8
DOIs	https://doi.org/10.1145/1008694.1008698
Publication status	Published - 2004
Externally published	Yes
Event	9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04 - Paris, France Duration: 13 Jun 2004 → 13 Jun 2004

Publication series

Name	Proceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)	0730-8078

Conference

Conference	9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04
Country/Territory	France
City	Paris
Period	13/06/04 → 13/06/04

Keywords

H.2.5 [DatabaseManagement]: Heterogeneous Databases
H.2.7 [Database Management]: Database Administration - Security, integrity, and protection
H.2.8 [Database Management]: Database Applications - Data mining
Security

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10.1145/1008694.1008698

Cite this

Clifton, C., Kantarcioǧlu, M., Doan, A. H., Schadow, G., Vaidya, J., Elmagarmid, A., & Suciu, D. (2004). Privacy-preserving data integration and sharing. In Workshop Proceedings - The 9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04 (pp. 19-26). (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/1008694.1008698

Clifton, Chris ; Kantarcioǧlu, Murat ; Doan, An Hai et al. / Privacy-preserving data integration and sharing. Workshop Proceedings - The 9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04. 2004. pp. 19-26 (Proceedings of the ACM SIGMOD International Conference on Management of Data).

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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.",

keywords = "H.2.5 [DatabaseManagement]: Heterogeneous Databases, H.2.7 [Database Management]: Database Administration - Security, integrity, and protection, H.2.8 [Database Management]: Database Applications - Data mining, Security",

author = "Chris Clifton and Murat Kantarcioǧlu and Doan, {An Hai} and Gunther Schadow and Jaideep Vaidya and Ahmed Elmagarmid and Dan Suciu",

year = "2004",

doi = "10.1145/1008694.1008698",

language = "English",

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series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",

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booktitle = "Workshop Proceedings - The 9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04",

note = "9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04 ; Conference date: 13-06-2004 Through 13-06-2004",

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Clifton, C, Kantarcioǧlu, M, Doan, AH, Schadow, G, Vaidya, J, Elmagarmid, A & Suciu, D 2004, Privacy-preserving data integration and sharing. in Workshop Proceedings - The 9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04. Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 19-26, 9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04, Paris, France, 13/06/04. https://doi.org/10.1145/1008694.1008698

Privacy-preserving data integration and sharing. / Clifton, Chris; Kantarcioǧlu, Murat; Doan, An Hai et al.
Workshop Proceedings - The 9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04. 2004. p. 19-26 (Proceedings of the ACM SIGMOD International Conference on Management of Data).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Clifton, Chris

AU - Kantarcioǧlu, Murat

AU - Doan, An Hai

AU - Schadow, Gunther

AU - Vaidya, Jaideep

AU - Elmagarmid, Ahmed

AU - Suciu, Dan

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AB - 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.

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KW - H.2.8 [Database Management]: Database Applications - Data mining

KW - Security

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Clifton C, Kantarcioǧlu M, Doan AH, Schadow G, Vaidya J, Elmagarmid A et al. Privacy-preserving data integration and sharing. In Workshop Proceedings - The 9th Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International Conference on Management of Data, SIGMOD-04. 2004. p. 19-26. (Proceedings of the ACM SIGMOD International Conference on Management of Data). doi: 10.1145/1008694.1008698

Privacy-preserving data integration and sharing

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