Private data discovery for privacy compliance in collaborative environments

From National Research Council Canada

Download	View accepted manuscript: Private data discovery for privacy compliance in collaborative environments (PDF, 310 KB)
DOI	Resolve DOI: https://doi.org/10.1007/978-3-540-88011-0_18
Author	Search for: Korba, Larry¹; Search for: Wang, Yunli¹; Search for: Geng, Liqiang¹; Search for: Song, Ronggong¹; Search for: Yee, George¹; Search for: Patrick, Andrew S.¹; Search for: Buffett, Scott¹; Search for: Liu, Hongyu¹; Search for: You, Yonghua¹
Name affiliation	National Research Council of Canada. NRC Institute for Information Technology
Format	Text, Book chapter
Proceedings title	Cooperative Design, Visualization, and Engineering: 5th International Conference, CDVE 2008 Calvià, Mallorca, Spain, September 21-25, 2008 Proceedings
Series title	Lecture Notes in Computer Science; no. 5220
Conference	Cooperative Design, Visualization, and Engineering, 5th International Conference (CDVE 2008), September 21-25, 2008, Palma de Mallorca, Spain
ISSN	0302-9743
Pages	142–150
Subject	collaborative computing; privacy; compliance; text mining; machine learning; privacy management; personally identifiable information; confidentialité; conformité; exploration de texte; apprentissage automatique; gestion des renseignements personnels; information personnellement identifiable
Abstract	With the growing use of computers and the Internet, it has become difficult for organizations to locate and effectively manage sensitive personally identifiable information (PII). This problem becomes even more evident in collaborative computing environments. PII may be hidden anywhere within the file system of a computer. As well, in the course of different activities, via collaboration or not, personally identifiable information may migrate from computer to computer. This makes meeting the organizational privacy requirements all the more complex. Our particular interest is to develop technology that would automatically discover workflow across organizational collaborators that would include private data. Since in this context, it is important to understand where and when the private data is discovered, in this paper, we focus on PII discovery, i.e. automatically identifying private data existant in semi-structured and unstructured (free text) documents. The first part of the process involves identifying PII via named entity recognition. The second part determines relationships between those entities based upon a supervised machine learning method. We present test results of our methods using publicly-available data generated from different collaborative activities to provide an assessment of scalability in cooperative computing environment.
Publication date	2008
Publisher	Springer Berlin Heidelberg Springer Berlin Heidelberg
Language	English
Peer reviewed	Yes
NRC number	NRCC 50386
NPARC number	8914078
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Record identifier	5007fa13-e850-4388-a48c-7a4fb76ccedb
Record created	2009-04-22
Record modified	2020-06-17

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Date modified:: 2021-05-08