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Privacy Enforcement in Data Analysis Workflows

by: Yolanda Gil, William Cheung, Varun Ratnakar, and Kai kin Chan

In: Proceedings of the Workshop on Privacy Enforcement and Accountability with Semantics PEAS2007 at ISWC/ASWC2007, Busan, South Korea (November 2007) .

Abstract

Collaborative e-Science projects commonly require data analysis to be performed on distributed data sets which may contain sensitive information. In addition to the credential-based privacy protection, ensuring proper handling of computerized data for disclosure and analysis is particularly essential in e-Science. In this paper, we propose a semantic approach for enforcing it through workflow systems. We define privacy preservation and analysis-relevant terms as ontologies and incorporate them into a proposed policy framework to represent and enforce the policies. We believe that workflow systems with the proposed privacy-awareness incorporated could ease the scientists in setting up privacy polices that suit for different types of collaborative research projects and can help them in safeguarding the privacy of sensitive data throughout the data analysis lifecycle.

BibTeX record

@inproceedings{Gil/2007/Privacy,
  abstract = {Collaborative e-Science projects commonly require data analysis to be performed on distributed data sets which may contain sensitive information. In addition to the credential-based privacy protection, ensuring proper handling of computerized data for disclosure and analysis is particularly essential in e-Science. In this paper, we propose a semantic approach for enforcing it through workflow systems. We define privacy preservation and analysis-relevant terms as ontologies and incorporate them into a proposed policy framework to represent and enforce the policies. We believe that workflow systems with the proposed privacy-awareness incorporated could ease the scientists in setting up privacy polices that suit for different types of collaborative research projects and can help them in safeguarding the privacy of sensitive data throughout the data analysis lifecycle.},
  added-at = {2007-11-07T19:19:39.000+0100},
  author = {Gil, Yolanda and Cheung, William and Ratnakar, Varun and kin Chan, Kai},
  biburl = {http://www.bibsonomy.org/bibtex/27d00d97db31431b65f0043766bb66efb/iswc2007},
  booktitle = {Proceedings of the Workshop on Privacy Enforcement and Accountability with Semantics (PEAS2007) at ISWC/ASWC2007, Busan, South Korea},
  crossref = {http://data.semanticweb.org/workshop/peas/2007/proceedings},
  editor = {Finin, Tim and Kagal, Lalana and Olmedilla, Daniel},
  interhash = {e15b4045d262d37a484c2fcbd0aa4914},
  intrahash = {7d00d97db31431b65f0043766bb66efb},
  keywords = {2007 analysis datum enforcement iswc privacy workflow workshop_peas},
  month = {November},
  timestamp = {2007-11-07T19:19:39.000+0100},
  title = {Privacy Enforcement in Data Analysis Workflows},
  year = 2007
}

Endnote record

%0 Conference Paper
%1 Gil/2007/Privacy
%A Gil, Yolanda
%A Cheung, William
%A Ratnakar, Varun
%A kin Chan, Kai
%B Proceedings of the Workshop on Privacy Enforcement and Accountability with Semantics (PEAS2007) at ISWC/ASWC2007, Busan, South Korea
%D 2007
%E Finin, Tim
%E Kagal, Lalana
%E Olmedilla, Daniel
%K 
%T Privacy Enforcement in Data Analysis Workflows
%X Collaborative e-Science projects commonly require data analysis to be performed on distributed data sets which may contain sensitive information. In addition to the credential-based privacy protection, ensuring proper handling of computerized data for disclosure and analysis is particularly essential in e-Science. In this paper, we propose a semantic approach for enforcing it through workflow systems. We define privacy preservation and analysis-relevant terms as ontologies and incorporate them into a proposed policy framework to represent and enforce the policies. We believe that workflow systems with the proposed privacy-awareness incorporated could ease the scientists in setting up privacy polices that suit for different types of collaborative research projects and can help them in safeguarding the privacy of sensitive data throughout the data analysis lifecycle.