Unifying Threats Against Information Integrity in Participatory Crowd Sensing

• Published in: IEEE pervasive computing Vol. 22; no. 4; pp. 1 - 10
• Main Authors: Bhattacharjee, Shameek, Das, Sajal K.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Decision making; Economics; Integrity; Sensors; Servers; Social networking (online); Threat modeling; Threat models; Urban areas
• ISSN: 1536-1268; 1558-2590
• DOI: 10.1109/MPRV.2023.3296271

This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making, or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack types and goals, underscoring formal definition, their relevance, and impact on the P-CS platform.