| Download | - View accepted manuscript: Private data discovery for privacy compliance in collaborative environments (PDF, 310 KB)
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| DOI | Resolve DOI: https://doi.org/10.1007/978-3-540-88011-0_18 |
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| 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 |
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| Name affiliation | - National Research Council Canada. NRC Institute for Information Technology
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| Format | Text |
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| Type | Book chapter |
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| Proceedings title | Cooperative Design, Visualization, and Engineering: 5th International Conference, CDVE 2008 Calvià, Mallorca, Spain, September 21-25, 2008 Proceedings |
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| Series title | Lecture Notes in Computer Science; no. 5220 |
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| Conference | Cooperative Design, Visualization, and Engineering, 5th International Conference (CDVE 2008), September 21-25, 2008, Palma de Mallorca, Spain |
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| ISSN | 0302-9743 |
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| Pages | 142–150 |
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| 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 |
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| 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. |
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| Publication date | 2008 |
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| Publisher | Springer Berlin Heidelberg |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRCC 50386 |
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| NPARC number | 8914078 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction |
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Record identifier | 5007fa13-e850-4388-a48c-7a4fb76ccedb | | Record created | 2009-04-22 |
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| Record modified | 2020-03-03 |
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