Security and Privacy Protection for Mobile Crowd Sensing

Dear Colleagues,

Mobile crowd sensing (MCS) has emerged as a powerful paradigm that leverages the sensing capabilities of ubiquitous smart devices to collect large-scale, fine-grained data for various intelligent applications, including urban monitoring, environmental assessment, public safety, and smart transportation. By integrating mobile terminals, wireless communication, and cloud platforms, MCS enables scalable and cost-effective data acquisition beyond the limitations of traditional sensing infrastructures. However, the openness of the sensing environment and the heavy reliance on human participants also expose MCS systems to substantial security and privacy risks, particularly when sensitive data are exchanged without adequate symmetric-key protection or lightweight encryption mechanisms.

Ensuring robust security and privacy protection in MCS is essential due to the sensitive nature of the collected data, which often involves users’ locations, mobility patterns, behavioral preferences, and even personal identities. Without appropriate safeguards, adversaries may launch various attacks such as data poisoning, spoofing, collusion, Sybil attacks, or inference attacks that compromise data integrity, degrade system reliability, or reveal private information. Furthermore, inadequate encryption—especially the absence of secure block cipher–based protection, authenticated symmetric encryption, or effective key-distribution mechanisms—can magnify the risk of unauthorized data exposure. Privacy threats such as trajectory re-identification, context inference, or profiling can severely erode worker trust and reduce participation willingness, ultimately undermining the utility of MCS applications.

To address these challenges, growing research efforts have focused on designing lightweight, scalable, and trustworthy protection mechanisms tailored for MCS. These approaches include secure participant recruitment, reliable worker authentication, privacy-preserving data reporting, robust truth discovery under adversarial conditions, encrypted or anonymized data aggregation, and the adoption of advanced privacy-enhancing technologies such as differential privacy, secure multi-party computation, and federated learning. Recently, the integration of lightweight symmetric-key cryptography, energy-efficient block ciphers, authenticated encryption with associated data (AEAD), and hybrid encryption frameworks has gained traction as a practical means to protect user-generated sensing data on resource-constrained devices. A critical goal is to balance high data utility and strong privacy guarantees while ensuring system resilience in dynamic, large-scale, and potentially adversarial environments.

The research community is now actively exploring new frameworks, attack models, and defense strategies to address the complex security and privacy challenges inherent in MCS. This topic welcomes contributions on theoretical foundations, system designs, algorithmic innovations, and real-world applications—including studies that incorporate symmetric-key protocols, key management for mobile devices, fast encryption schemes, and lightweight cryptographic modules to strengthen sensing security.

Areas of interest include, but are not limited to, the following:

1. Security authentication and access control in internet of vehicles;
2. Robust participant authentication and permission management for crowd-sensed systems;
3. Secure transmission protocols and communication protection for large-scale MCS platforms;
4. Human-centric identity verification and adaptive access governance in crowd sensing;
5. Privacy-aware computational frameworks for mobility-driven sensing applications;
6. Enhancing data confidentiality in federated learning through advanced privacy techniques;
7. Protection models for safeguarding sensitive information in user-contributed sensing data;
8. Trustworthy foundation models and privacy-conscious large models for MCS tasks;
9. Secure knowledge extraction and privacy-preserving mining from massive sensing datasets;
10. Techniques for safeguarding location and trajectory information in crowd-based sensing;
11. Security hardening and privacy protection for power-efficient sensing and mobile energy services.

Prof. Dr. Yining Liu
Dr. Feifei Kou
Dr. Jiwei Zhang
Dr. Lei Shi
Dr. Jun Feng
Dr. Pengfei Zhang
Guest Editors

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• mobile crowd sensing (MCS)
• symmetric-key protocols
• key management for mobile devices