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Analysis and text classification of privacy policies from rogue and top-100 fortune global companies
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-9316-4842
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2019 (English)In: International Journal of Information Security and Privacy, ISSN 1930-1650, E-ISSN 1930-1669, Vol. 13, no 2, p. 47-66Article in journal (Refereed) Published
Abstract [en]

In the present article, the authors investigate to what extent supervised binary classification can be used to distinguish between legitimate and rogue privacy policies posted on web pages. 15 classification algorithms are evaluated using a data set that consists of 100 privacy policies from legitimate websites (belonging to companies that top the Fortune Global 500 list) as well as 67 policies from rogue websites. A manual analysis of all policy content was performed and clear statistical differences in terms of both length and adherence to seven general privacy principles are found. Privacy policies from legitimate companies have a 98% adherence to the seven privacy principles, which is significantly higher than the 45% associated with rogue companies. Out of the 15 evaluated classification algorithms, Naïve Bayes Multinomial is the most suitable candidate to solve the problem at hand. Its models show the best performance, with an AUC measure of 0.90 (0.08), which outperforms most of the other candidates in the statistical tests used. Copyright © 2019, IGI Global.

Place, publisher, year, edition, pages
IGI Global , 2019. Vol. 13, no 2, p. 47-66
Keywords [en]
Classification, Classification algorithms, Information security, Machine learning, Privacy policies, Privacy policy data set, Data mining, Data privacy, Learning systems, Security of data, Text processing, Websites, Binary classification, Classification algorithm, Data set, Fortune global 500, Privacy principle, Statistical differences, Text classification, Classification (of information)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-17875DOI: 10.4018/IJISP.2019040104ISI: 000467764600004Scopus ID: 2-s2.0-85064536690OAI: oai:DiVA.org:bth-17875DiVA, id: diva2:1313083
Available from: 2019-05-02 Created: 2019-05-02 Last updated: 2019-06-13Bibliographically approved

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Boldt, Martin

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  • apa
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