Artikel in Tagungsband INPROC-2024-02

Bibliograph.
Daten
Li, Yunxuan; Stach, Christoph; Mitschang, Bernhard: PaDS: An adaptive and privacy-enabling Data Pipeline for Smart Cars.
In: Renso, Chiara (Hrsg); Sakr, Mahmoud (Hrsg); Aref, Walid G (Hrsg); Kim, Kyoung-Sook (Hrsg); Papagelis, Manos (Hrsg); Sacharidis, Dimitris (Hrsg): Proceedings of the 25th IEEE International Conference on Mobile Data Management (MDM 2024).
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik.
S. 41-50, englisch.
Los Alamitos, Washington, Tokyo: IEEE Computer Society Conference Publishing Services, Juni 2024.
ISBN: 979-8-3503-7455-1; ISSN: 2375-0324; DOI: 10.1109/MDM61037.2024.00026.
Artikel in Tagungsband (Konferenz-Beitrag).
CR-Klassif.K.4.1 (Computers and Society Public Policy Issues)
Keywordssmart car; privacy-enabling data pipeline; datastream runtime adaptation; mobile data privacy management
Kurzfassung

The extensive use of onboard sensors in smart cars enables the collection, processing, and dissemination of large amounts of mobile data containing information about the vehicle, its driver, and even bystanders. Despite the undoubted benefits of such smart cars, this leads to significant privacy concerns. Due to their inherent mobility, the situation of smart cars changes frequently, and with it, the appropriate measures to counteract the exposure of private data. However, data management in such vehicles lacks sufficient support for this privacy dynamism. We therefore introduce PaDS, a framework for Privacy adaptive Data Stream. The focus of this paper is to enable adaptive data processing within the vehicle data stream. With PaDS, Privacy-Enhancing Technologies can be deployed dynamically in the data pipeline of a smart car according to the current situation without user intervention. With a comparison of state-of-the-art approaches, we demonstrate that our solution is very efficient as it does not require a complete restart of the data pipeline. Moreover, compared to a static approach, PaDS causes only minimal overhead despite its dynamic adaptation of the data pipeline to react to changing privacy requirements. This renders PaDS an effective privacy solution for smart cars.

CopyrightCopyright © 2024 IEEE by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
KontaktSenden Sie eine E-Mail an <yunxuan.li@ipvs.uni-stuttgart.de>.
Abteilung(en)Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Anwendersoftware
Projekt(e)SofDCar
Eingabedatum16. Juli 2024
   Publ. Informatik