Abstract
Confidential information is all too easily leaked by naive users posting comments. In this paper we introduce DUIL, a system for Detecting Unintentional Information Leakers. The value of DUIL is in its ability to detect those responsible for information leakage that occurs through comments posted on news articles in a public environment, when those articles have withheld material non-public information. DUIL is comprised of several artefacts, each designed to analyse a different aspect of this challenge: the information, the user(s) who posted the information, and the user(s) who may be involved in the dissemination of information. We present a design science analysis of DUIL as an information system artefact comprised of social, information, and technology artefacts. We demonstrate the performance of DUIL on real data crawled from several Facebook news pages spanning two years of news articles.
Original language | English |
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Pages (from-to) | 470-485 |
Number of pages | 16 |
Journal | European Journal of Information Systems |
Volume | 27 |
Issue number | 4 |
DOIs | |
State | Published - 4 Jul 2018 |
Bibliographical note
Publisher Copyright:© 2018, © 2018 Operational Research Society.
Funding
Mauro Conti is supported by a Marie Curie Fellowship funded by the European Commission under the agreement No. PCIG11-GA-2012-321980. This work is also partially supported by the TENACE PRIN Project 20103P34XC funded by the Italian MIUR, and by the Project “Tackling Mobile Malware with Innovative Machine Learning Techniques” funded by the University of Padua. This research was partially funded by Israel Ministry of Science and Technology research grant 3-9770 Data Leakage in Social Networks: Detection and Prevention. Mauro Conti is supported by a Marie Curie Fellowship funded by the European Commission under the agreement No. PCIG11-GA-2012-321980. This work is also partially supported by the TENACE PRIN Project 20103P34XC funded by the Italian MIUR, and by the Project ?Tackling Mobile Malware with Innovative Machine Learning Techniques? funded by the University of Padua. This research was partially funded by Israel Ministry of Science and Technology research grant 3-9770 Data Leakage in Social Networks: Detection and Prevention.
Funders | Funder number |
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Detection and Prevention | |
Innovative Machine Learning Techniques? | |
Israel Ministry of Science and Technology | |
University of Padua | |
Cancer Center for Detection and Prevention | |
European Commission | PCIG11-GA-2012-321980 |
Ministero dell’Istruzione, dell’Università e della Ricerca | |
Università degli Studi di Padova | |
Ministry of science and technology, Israel |
Keywords
- Cybersecurity
- design science research
- information leakers
- online social networks
- sensitive information
- threat detection