Krüger, Nicolai; Stibe, Agnis; Teuteberg, Frank
The Black Mirror: What Your Mobile Phone Number Reveals About You. Konferenzberichte
Springer, Cham, Bd. 389, 2020, ISBN: 978-3-030-53337-3.
Abstract | Links | BibTeX | Schlagwörter: A, Information privacy, Mobile Device Management, Mobile phone privacy, Privacy, Privacy scoring model, Social media privacy
@proceedings{Krüger2020,
title = {The Black Mirror: What Your Mobile Phone Number Reveals About You.},
author = {Nicolai Krüger and Agnis Stibe and Frank Teuteberg},
editor = {Witold Abramowicz and Gary Klein},
url = {https://doi.org/10.1007/978-3-030-53337-3_2},
doi = {10.1007/978-3-030-53337-3_2},
isbn = {978-3-030-53337-3},
year = {2020},
date = {2020-06-08},
urldate = {2020-11-04},
booktitle = {Business Information Systems, 23rd International Conference, BIS 2020, Colorado Springs, CO, USA},
volume = {389},
pages = {18–32},
publisher = {Springer},
address = {Cham},
series = {Lecture Notes in Business Information Processing},
abstract = {In the present era of pervasive mobile technologies, interconnecting innovations are increasingly prevalent in our lives. In this evolutionary process, mobile and social media communication systems serve as a backbone for human interactions. When assessing privacy risks related to this, privacy scoring models (PSM) can help quantifying the personal information risks. This paper uses the mobile phone number itself as a basis for privacy scoring. We tested 1,000 random phone numbers for their matching to social media accounts. The results raise concerns how network and communication layers are predominately connected. PSMs will support future organizational sensitivity for data linkability.},
keywords = {A, Information privacy, Mobile Device Management, Mobile phone privacy, Privacy, Privacy scoring model, Social media privacy},
pubstate = {published},
tppubtype = {proceedings}
}
In the present era of pervasive mobile technologies, interconnecting innovations are increasingly prevalent in our lives. In this evolutionary process, mobile and social media communication systems serve as a backbone for human interactions. When assessing privacy risks related to this, privacy scoring models (PSM) can help quantifying the personal information risks. This paper uses the mobile phone number itself as a basis for privacy scoring. We tested 1,000 random phone numbers for their matching to social media accounts. The results raise concerns how network and communication layers are predominately connected. PSMs will support future organizational sensitivity for data linkability.