Inhalt des Dokuments
Prof. Dr. Axel Küpper
Axel Küpper is a professor for Service-centric Networking at Technische Universität Berlin (TU Berlin) and Telekom Innovation Laboratories, which is a public-private partnership of Deutsche Telekom AG and TU Berlin.
Before, he was an assistant professor at the Mobile and Distributed Systems Group at Ludwig-Maximilians-Universität München, where he received his postdoctoral lecture qualification (habilitation). He graduated in computer science from RWTH Aachen University, where he also received his doctorate.
Axel's early research focused on distributed and mobile systems and networks, context-aware applications, and location-based services. In recent years, he has worked intensively on topics related to Cloud Computing, Service-oriented Architectures, and future web technologies. Axel is an advocate of decentralized systems and applications. The focus of his work here is on distributed ledger technologies, token economy, self-sovereign identity, emerging blockchain-based applications, blockchain analytics, and decentralized online social networks.
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Kindly note that we can not provide office hours on March 18, 2021 due to oral exams taking place.
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Publications
Citation key | 10.1007/978-3-030-21752-5_1 |
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Author | Raschke, P. and Zickau, S. and Kröger, J. L. and Küpper, A. |
Title of Book | Privacy Technologies and Policy |
Pages | 3–17 |
Year | 2019 |
ISBN | 978-3-030-21752-5 |
Address | Cham |
Editor | Naldi, Maurizio and Italiano, Giuseppe F. and Rannenberg, Kai and Medina, Manel and Bourka, Athena |
Publisher | Springer International Publishing |
Abstract | Targeted advertising is an inherent part of the modern Web as we know it. For this purpose, personal data is collected at large scale to optimize and personalize displayed advertisements to increase the probability that we click them. Anonymity and privacy are also important aspects of the World Wide Web since its beginning. Activists and developers relentlessly release tools that promise to protect us from Web tracking. Besides extensive blacklists to block Web trackers, researchers used machine learning techniques in the past years to automatically detect Web trackers. However, for this purpose often artificial data is used, which lacks in quality. |
Bibtex Type of Publication | SNET Social |
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