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2. Preface.

Author

Tamara Munzner, Niklas Elmqvist, Holger Theisel, Matthew Kay 0001, Adam Perer, Tatiana von Landesberger, Jiawan Zhang, Christoph Garth, Chaoli Wang 0001, Pierre Dragicevic, Daniel F. Keefe, Filip Sadlo, Ivan Viola, Wenwen Dou, and Steffen Koch 0001

Published
2025
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3. Case Study on Privacy-Aware Social Media Data Processing in Disaster Management

Author

Marc Löchner, Ramian Fathi, David ‘-1’ Schmid, Alexander Dunkel, Dirk Burghardt, Frank Fiedrich, and Steffen Koch

Subjects
disaster management, virtual operations support teams, privacy, data retention, hyperloglog, focus group discussion, Geography (General), G1-922
Abstract

Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. In these critical situations, it is not surprising that privacy is often considered a secondary problem. In order to prevent subsequent abuse, theft or public exposure of collected datasets, however, protecting the privacy of social media users is crucial. Avoiding unnecessary data retention is an important question that is currently largely unsolved. There are a number of technical approaches available, but their deployment in disaster management is either impractical or requires special adaption, limiting its utility. In this case study, we explore the deployment of a cardinality estimation algorithm called HyperLogLog into disaster management processes. It is particularly suited for this field, because it allows to stream data in a format that cannot be used for purposes other than the originally intended. We develop and conduct a focus group discussion with teams of social media analysts. We identify challenges and opportunities of working with such a privacy-enhanced social media data format and compare the process with conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisition process will not distract the data analysis process. Instead, several benefits, such as improved working with huge datasets, may contribute to a more widespread use and adoption of the presented technique, which provides a basis for a better integration of privacy considerations in disaster management.

Published
2020
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18. Intelligent Methods for Test and Reliability.

Author

Hussam Amrouch, Jens Anders, Steffen Becker 0001, Maik Betka, Gerd Bleher, Peter Domanski, Nourhan Elhamawy, Thomas Ertl, Athanasios Gatzastras, Paul R. Genssler, Sebastian Hasler, Martin Heinrich, André van Hoorn, Hanieh Jafarzadeh, Ingmar Kallfass, Florian Klemme, Steffen Koch 0001, Ralf Küsters, Andrés Lalama, Raphaël Latty, Yiwen Liao, Natalia Lylina, Zahra Paria Najafi-Haghi, Dirk Pflüger, Ilia Polian, Jochen Rivoir, Matthias Sauer 0002, Denis Schwachhofer, Steffen Templin, Christian Volmer, Stefan Wagner 0001, Daniel Weiskopf, Hans-Joachim Wunderlich, Bin Yang 0009, and Martin Zimmermann

Published
2022
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