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PrivacyDates: A Framework for More Privacy-Preserving Timestamp Data Types

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2022

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Gesellschaft für Informatik, Bonn

Zusammenfassung

Case studies of application software data models indicate that timestamps are excessively used in connection with user activity. This contradicts the principle of data minimisation which demands a limitation to data necessary for a given purpose. Prior work has also identified common purposes of timestamps that can be realised by more privacy-preserving alternatives like counters and dates with purpose-oriented precision. In this paper, we follow up by demonstrating the real-world applicability of those alternatives. We design and implement three timestamp alternatives for the popular web development framework Django and evaluate their practicality by replacing conventional timestamps in the project management application Taiga.

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Burkert, Christian; Balack, Jonathan; Federrath, Hannes (2022): PrivacyDates: A Framework for More Privacy-Preserving Timestamp Data Types. GI SICHERHEIT 2022. DOI: 10.18420/sicherheit2022_06. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-717-3. pp. 101-112. Session 2. Karlsruhe. 5.-8. April 2022

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