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How to protect my privacy? - Classifying end-user information privacy protection behaviors

: Ebbers, Frank


Friedewald, Michael (Ed.); Önen, Melek (Ed.); Lievens, Eva (Ed.); Krenn, Stephan (Ed.); Fricker, Samuel (Ed.) ; International Federation for Information Processing -IFIP-:
Privacy and identity management. Data for better living: AI and privacy : 14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2. International Summer School, Windisch, Switzerland, August 19-23, 2019, Revised Selected Papers
Cham: Springer International Publishing, 2020 (IFIP advances in information and communication technology 576)
ISBN: 978-3-030-42503-6
ISBN: 978-3-030-42504-3
DOI: 10.1007/978-3-030-42504-3
Summer School on Privacy and Identity Management "Data for Better Living - Artificial Intelligence and Privacy" <14, 2019, Brugg/Windisch>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)

Forum Privatheit. Forschung für ein selbstbestimmtes Leben in der digitalen Welt
Conference Paper
Fraunhofer ISI ()
privacy protection; protection behavior; protection activities; privacy responses; Taxonomy; classification; model; user-centric

The Internet and smart devices pose many risks at users’ information privacy. Individuals are aware of that and try to counter tracking activities by applying different privacy protection behaviors. These are manifold and differ in scope, goal and degree of technology utilization. Although there is a lot of literature which investigates protection strategies, it is lacking holistic user-centric classifications. We review literature and identify 141 privacy protection behaviors end-users show.
We map these results to 38 distinct categories and apply hybrid cart sorting to create a taxonomy, which we call the “End-User Information Privacy Protection Behavior Model” (EIPPBM).