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E-Mail Tracking

  • E-mail advertisement, as one instrument in the marketing mix, allows companies to collect fine-grained behavioural data about individual users’ e-mail reading habits realised through sophisticated tracking mechanisms. Such tracking can be harmful for user privacy and security. This problem is especially severe since e-mail tracking techniques gather data without user consent. Striving to increase privacy and security in e-mail communication, the paper makes three contributions. First, a large database of newsletter e-mails is developed. This data facilitates investigating the prevalence of e- mail tracking among 300 global enterprises from Germany, the United Kingdom and the United States. Second, countermeasures are developed for automatically identifying and blocking e-mail tracking mechanisms without impeding the user experience. The approach consists of identifying important tracking descriptors and creating a neural network-based detection model. Last, the effectiveness of the proposed approach is established by means ofE-mail advertisement, as one instrument in the marketing mix, allows companies to collect fine-grained behavioural data about individual users’ e-mail reading habits realised through sophisticated tracking mechanisms. Such tracking can be harmful for user privacy and security. This problem is especially severe since e-mail tracking techniques gather data without user consent. Striving to increase privacy and security in e-mail communication, the paper makes three contributions. First, a large database of newsletter e-mails is developed. This data facilitates investigating the prevalence of e- mail tracking among 300 global enterprises from Germany, the United Kingdom and the United States. Second, countermeasures are developed for automatically identifying and blocking e-mail tracking mechanisms without impeding the user experience. The approach consists of identifying important tracking descriptors and creating a neural network-based detection model. Last, the effectiveness of the proposed approach is established by means of empirical experimentation. The results suggest a classification accuracy of 99.99%.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Benedict BenderORCiDGND, Benjamin FabianORCiDGND, Stefan Lessmann, Johannes HauptORCiD
URL:https://aisel.aisnet.org/icis2016/ISSecurity/Presentations/13/
Titel des übergeordneten Werks (Englisch):Proceedings of the 37th International Conference on Information Systems (ICIS)
Untertitel (Englisch):status quo and novel countermeasures
Publikationstyp:Konferenzveröffentlichung
Sprache:Englisch
Datum der Erstveröffentlichung:21.11.2016
Erscheinungsjahr:2016
Datum der Freischaltung:14.11.2022
Freies Schlagwort / Tag:Countermeasures; E-Mail Tracking; Machine Learning; Privacy; Security
Seitenanzahl:19
Organisationseinheiten:Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre
DDC-Klassifikation:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
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