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DataSys 2023 Congress June 26, 2023 to June 30, 2023 - Nice, Saint-Laurent-du-Var, France
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ComputationWorld 2023 Congress June 26, 2023 to June 30, 2023 - Nice, Saint-Laurent-du-Var, France
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NetWare 2023 Congress September 25, 2023 to September 29, 2023 - Porto, Portugal
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- SECURWARE 2023, The Seventeenth International Conference on Emerging Security Information, Systems and Technologies
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ThinkMind // SECURWARE 2023, The Seventeenth International Conference on Emerging Security Information, Systems and Technologies // View article securware_2023_1_150_30092
Science-Tracker Fingerprinting with Uncertainty: Selected Common Characteristics of Publishers from Network to Application Trackers on the Example of Web, App and Email
 Authors: Stefan Kiltz Robert Altschaffel Jana Dittmann Keywords: Security, trust and privacy metrics; IT forensics; Attribution Abstract: Science tracking in information systems provided by publishers is a common pattern in today's scientific world. Besides impacting on the privacy of researchers, science tracking can have potentially adverse or even grave consequences for researchers. The comparison of tracking mechanisms employed by publishers can lead to interesting findings about their popularity over time and to better fine-tuned countermeasures. Publisher could also be identified according to the tracking mechanisms employed. Studies concerning user tracking in general and in particular science tracking exist. The approaches considered therein are concerned with detecting whether tracking is employed on the example of Web, App and email. Our goals are to allow a better comparison of publisher tracking as a privacy measurement by also including results from different forensic tools and to get hints/leads to individualize (attribute) science trackers. Towards our goals we introduce a BNF-style expression based Science-Tracking Fingerprint (STF) as semantics to support individualizing a publisher and for privacy measurements generally. Syntactically, a vector consisting of element-value pairs is created. We propose a certainty category as a metric in support of privacy measurement to rank privacy measurements as plausible, uncertain, or non-match. We investigate intra- and inter-application matches per publisher on URL and tracker information of existing and adapted static tools (of-/on-premises). We analyze 4 exemplary chosen science publishers in 3 selected application areas (web, app, email) with 4 known tools for web, 3 known tools for apps and 2 known tools for email. For the latter, we also introduce a self-implemented tool. We show that the STF is, according to our first tests, fit for the purpose of comparing of publisher tracking and to provide hints/leads for individualization of publishers employing science tracking. Pages: 88 to 97 Copyright: Copyright (c) IARIA, 2023 Publication date: September 25, 2023 Published in: conference ISSN: 2162-2116 ISBN: 978-1-68558-092-6 Location: Porto, Portugal Dates: from September 25, 2023 to September 29, 2023
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