Trust, Privacy and Security in Digital Business

17th International Conference, TrustBus 2020, Bratislava, Slovakia, September 14¿17, 2020, Proceedings
1. Auflage 2020
ISBN: 978-3-030-58985-1
Verlag: Springer International Publishing

Buch, Englisch, Band 12395, 235 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 382 g

Reihe: Lecture Notes in Computer Science

This book constitutes the refereed proceedings of the 17th International Conference on Trust, Privacy and Security in Digital Business, TrustBus 2020, held in Bratislava, Slovakia, in September 2020. The conference was held virtually due to the COVID-19 pandemic.
The 11 full and 4 short papers presented were carefully reviewed and selected from 28 submissions. The papers are organized in the following topical sections: blockchain, cloud security/hardware; economics/privacy; human aspects; privacy; privacy and machine learning; trust.

Zielgruppe

Research

Autoren/Hrsg.

Gritzalis, Stefanos

Weippl, Edgar R.

Khalil, Ismail

Kotsis, Gabriele

Tjoa, A Min

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management

Inhaltsverzeichnis

Analysis of uPort Open, an identity management blockchain-based solution.- Cloud Computing Framework for e-Health Security Requirements & Security Policy Rules Case Study: A European Cloud-based Health System.- On the Suitability of Using SGX for Secure Key Storage in the Cloud.- Employment of Secure Enclaves in CheatDetection Hardening.- SECONDO: A Platform for Cybersecurity Investments and Cyber Insurance Decisions.- Are Sensor Based Business Models a Threat to Privacy: The Case Of Pay-How-Your-Drive Insurance Models.- Microtargeting or Microphishing? Phishing Unveiled.- Privacy - Preserving Service Composition with Enhanced Flexibility and Efficiency.- An empirical Investigation of the right to explanation under GDPR in insurance.- Measuring users’ socio-contextual attributes for Self-Adaptive Privacy within Cloud-Computing Environments.- Empowering Users Through a PrivacyMiddleware Watchdog.- Utility Requirement Description for Utility-preserving and Privacy-respecting Data Pseudonymization.- DEFeND DSM: A Data Scope Management Service for Model-Based Privacy by Design GDPR Compliance.- A Distributed Trust Framework for Privacy-Preserving Machine Learning.- A Fuzzy Trust Model for Autonomous Entities Acting in Pervasive Computing.