Privacy and Security for Mobile Crowdsourcing

1. Auflage 2023
ISBN: 978-87-7022-861-9
Verlag: River Publishers

Buch, Englisch, 142 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 385 g

Reihe: River Publishers Series in Digital Security and Forensics

This concise guide to mobile crowdsourcing and crowdsensing vulnerabilities and countermeasures walks readers through a series of examples, discussions, tables, initiative figures, and diagrams to present to them security and privacy foundations and applications. Discussed approaches help build intuition to apply these concepts to a broad range of system security domains toward dimensioning of next generations of mobiles crowdsensing applications. This book offers vigorous techniques as well as new insights for both beginners and seasoned professionals. It reflects on recent advances and research achievements.

Technical topics discussed in the book include but are not limited to:

- Risks affecting crowdsensing platforms

- Spatio-temporal privacy of crowdsourced applications

- Differential privacy for data mining crowdsourcing

- Blockchain-based crowdsourcing

- Secure wireless mobile crowdsensing.

This book is accessible to readers in mobile computer/communication industries as well as academic staff and students in computer science, electrical engineering, telecommunication systems, business information systems, and crowdsourced mobile app developers.

Zielgruppe

Postgraduate and Professional Practice & Development

Autoren/Hrsg.

Sodagari, Shabnam

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Naturwissenschaften Physik Mechanik Energie
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit

Inhaltsverzeichnis

1. The Importance of Crowdsourcing 2. Spatio-temporal Privacy of Crowdsourced Applications 3. Differentially Private Mobile Crowdsourcing 4. Trust in Edge-and-fog-based Vehicular Crowdsensing 5. Blockchain-based Solutions for Security and Privacy of MCS Systems 6. MCS Security Games and Incentive Mechanisms 7. Machine Learning Based Privacy/Security Solutions for MCS 135 8. Crowdsourced Mobile Apps 9. Reliable Industrial IoT Using Crowdsourcing 10. Misinformation, Fake News, and Crowdsourcing 11. Security in 6G and Wi-Fi Communications Leveraging Mobile Crowdsensing 12. Problems

Über Autor(innen)

Shabnam Sodagari received her Ph.D. from the Pennsylvania State University in electrical engineering and is a faculty member of computer engineering and computer science.