Abstract
Mobile crowdsensing (MCS) leverages smart devices for diverse data collection tasks, ranging from noise measurements to traffic congestion levels. However, with security and privacy a prerequisite for deployment, creating a diverse ecosystem, considering user specifics, providing adequate privacy to task initiators, and enhancing user control are key factors for MCS systems to achieve their full potential. We introduce our secure and privacy-preserving architecture for MCS, designed to address these challenges, improving user control, relevance, and privacy. Our work utilizes a variant of identity-based encryption to capture user characteristics and attributes, enabling secure task enrollment and eligibility enforcement while reinforcing task initiator privacy. This study emphasizes modularity as a design goal, enabling system entities to function without relying upon others while supporting all security and privacy requirements of MCS stakeholders. We finally evaluate feasibility and efficiency to show that the proposed system is practical.
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Notes
- 1.
Such attacks are beyond the scope of this work.
- 2.
The construction of these misbehavior detection methods is beyond the scope of this work and therefore is not discussed here.
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Eryonucu, C., Papadimitratos, P. (2024). Security and Privacy for Mobile Crowdsensing: Improving User Relevance and Privacy. In: Katsikas, S., et al. Computer Security. ESORICS 2023 International Workshops. ESORICS 2023. Lecture Notes in Computer Science, vol 14398. Springer, Cham. https://doi.org/10.1007/978-3-031-54204-6_28
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