Tailoring trusted semantic information
Article No.: 17, Pages 1 - 4
Abstract
Tailored trusted spaces (TTS) need to identify data sensitive to user context and ensure sensitive data is not leaked. This task is similar to Data Leak Prevention (DLP) solutions, which monitor and control corporate data flow. Current DLP techniques find data that matches user defined syntactic patterns [1, 2]. However, user context is usually described by information semantics, rather than data syntax. In this paper, we propose a method which extracts semantic features from a small number of training documents describing user context, and use the presence of these semantic features to decide whether an input document contains sensitive information or not. Test results from three document sets show the proposed method giving 96%, 90%, 75% true positive and 3%, 14%, 26% false positive rates.
References
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Index Terms
- Tailoring trusted semantic information
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Published In
January 2013
282 pages
ISBN:9781450316873
DOI:10.1145/2459976
Copyright © 2013 Authors.
Sponsors
- Los Alamos National Labs: Los Alamos National Labs
- Sandia National Labs: Sandia National Laboratories
- DOE: Department of Energy
- Oak Ridge National Laboratory
- Lawrence Livermore National Lab.: Lawrence Livermore National Laboratory
- BERKELEYLAB: Lawrence National Berkeley Laboratory
- Argonne Natl Lab: Argonne National Lab
- Idaho National Lab.: Idaho National Laboratory
- Pacific Northwest National Laboratory
- Nevada National Security Site: Nevada National Security Site
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 January 2013
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- Research-article
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CSIIRW '13
Sponsor:
- Los Alamos National Labs
- Sandia National Labs
- DOE
- Lawrence Livermore National Lab.
- BERKELEYLAB
- Argonne Natl Lab
- Idaho National Lab.
- Nevada National Security Site
CSIIRW '13: Cyber Security and Information Intelligence
January 8 - 10, 2013
Tennessee, Oak Ridge, USA
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