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Editorial

Data Privacy and Cybersecurity in Mobile Crowdsensing

by
Chuan Zhang
1,*,
Tong Wu
2 and
Weiting Zhang
3
1
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
2
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
3
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(5), 1038; https://doi.org/10.3390/electronics14051038
Submission received: 19 February 2025 / Accepted: 27 February 2025 / Published: 5 March 2025
(This article belongs to the Special Issue Data Privacy and Cybersecurity in Mobile Crowdsensing)
Versions Notes
Mobile crowdsensing (MCS) has emerged as a pivotal element in contemporary communication technology, witnessing substantial growth recently. The advent of 5G [1], the Internet of Things (IoT) [2], and edge computing [3] has propelled MCS to achieve enhanced sensing efficiency and broaden its application spectrum across various domains such as environmental monitoring [4], traffic management [5], and healthcare [6].
However, despite these advantages, MCS confronts significant security and privacy challenges due to its open and diverse nature. Critical concerns encompass data leakage [7,8,9,10], unauthorized access [11,12], data tampering [13,14], and cross-network attacks [15,16]. These issues can severely compromise the stability, privacy, and security of MCS systems. Furthermore, the dynamic mobility of users and devices within MCS introduces additional complexity to conventional security measures, particularly concerning communication and cross-domain access control [17,18].
To tackle these challenges, researchers have devised several strategies aimed at bolstering the security and privacy of MCS systems [19,20,21]. These novel protection mechanisms offer distinct benefits over traditional approaches. They are capable of securing data even with constrained computational and communication resources, enhancing system flexibility, and effectively thwarting sophisticated cyberattacks. These strategies provide both theoretical and practical underpinnings for fortifying MCS security and lay a robust foundation for the field’s future evolution.
As concerns over data privacy and cybersecurity increase [22,23], numerous researchers have proffered innovative solutions in the realms of mobile sensing, the Internet of Things (IoT), and federated learning, with the aim of safeguarding data privacy and bolstering system security. Regarding the protection mechanism for UAV network task chains, Contribution 1 introduces a UAV network mission chain protection mechanism grounded in vulnerability analysis and topology reconfiguration. By employing the multi-indicator node vulnerability assessment algorithm and the node importance-based topology reconfiguration algorithm, this research can effectively pinpoint critical nodes and optimize network topology to enhance both reliability and security. In the domain of recommender systems, Contribution 2 advances a differential privacy-based federated recommendation framework that safeguards user privacy while preserving recommendation accuracy through the addition of noise to model updates. The study substantiates the efficacy of the framework across various datasets experimentally. In Contribution 3, the authors propose an innovative federated recommendation framework that integrates differential privacy techniques to protect user privacy without compromising recommendation accuracy. To fortify privacy protection further, the authors also devise a specialized differential privacy algorithm for ensuring that individual user information cannot be inferred from the global model by meticulously calibrated noise added to aggregated data updates. Moreover, addressing privacy protection challenges within the Internet of Vehicles [24,25], Contribution 4 puts forth a consensus mechanism based on proof-of-multiple-state. This mechanism leverages blockchain technology to resolve data security and privacy issues in vehicular networking while optimizing communication efficiency among mobile nodes. The PoMS mechanism utilizes vehicle driving state predictions and machine learning models to dynamically adjust node weights and select optimal relay nodes, thereby enhancing both the efficiency and security of network communications.
In the realm of affective speech generation, Contribution 5 introduces a method for generating high-quality affective speech data by integrating affective embedding and phonological style embedding. Utilizing the Mel spectrogram as an input, this method generates affective speech through a diffusion process. Addressing challenges in mobile sensors and localization problems, Contribution 7 proposes a multi-source sparse inverse localization technique based on mobile sensors. This approach targets signal source localization issues within long-distance mobile sensor networks. Additionally, a long-range localization method grounded in the block sparse model is presented, along with a unilateral branching ratio decision algorithm designed to adaptively control the iterative process amidst unknown sparsity levels. Building upon these advancements, Contribution 6 presents a swell neural network algorithm tailored for resolving time-varying path query problems while considering privacy protection. The algorithm efficiently identifies multiple paths, including the shortest path, while safeguarding user privacy. It also designs an encrypted indexing scheme to effectively prevent the leakage of user information. Targeting concerns related to privacy breaches and malicious disruptions in electronic voting systems [26,27], Contribution 8 proposes an efficient e-voting system leveraging homomorphic encryption. An enhanced HSE-Voting system is designed, which fortifies system security by incorporating signatures and partial proof-of-knowledge protocols, simultaneously reducing computational costs for proxies. This improved system ensures the accuracy and verifiability of election outcomes while preserving voter privacy.
To tackle the escalating issue of copyright infringement in models [28,29], Contribution 9 introduces a framework named PTFCP. This framework is meticulously designed to counteract the burgeoning threat of model copyright infringement. By leveraging cryptographic technologies, PTFCP facilitates privacy-preserving similarity assessments between victim and suspect models without disclosing sensitive details. In genomics research, Contribution 10 presents MLPPF, a method for the multi-tagging prediction of piRNA function. MLPPF demonstrates its efficacy by efficiently identifying mRNA-associated and lncRNA-associated markers of piRNA function, thereby revealing pivotal elements within piRNA sub-sequences. Regarding WiFi gesture recognition, Contribution 11 addresses cross-domain challenges through an innovative combination of knowledge distillation and Jensen–Shannon scattering techniques. Within the domain of mobile sensors, Contribution 12 proposes a secure certificate signing scheme tailored for resource-constrained devices. This scheme innovates with a novel public key structure that obviates the need for pairing operations. Furthermore, Contribution 13 unveils a pioneering IoT intrusion detection scheme. This scheme harnesses a cutting-edge neural network algorithm to adeptly manage large-scale, heterogeneous, high-dimensional, and time-dependent IoT network traffic data. Lastly, addressing the key management conundrum in identity-based cryptography, Contribution 14 advances a new certificateless signature scheme. This scheme extends the SM2 algorithm into a certificateless context, effectively resolving key management issues while enabling the batch verification of multiple signatures.
In the realm of big data management for IoT, Contribution 15 advances a novel strategy dedicated to safeguarding privacy across extensive networks of systems. This approach ensures the robust protection of both data and model privacy through the synergistic integration of distributed model training and secure model aggregation. Addressing multiple ciphertext equivalence testing, Contribution 16 introduces a certificateless encryption concept that facilitates multiple ciphertext equivalence testing with agent-assisted authorization. The proposed scheme fortifies security by enabling concurrent equivalence tests on numerous ciphertexts while incorporating an innovative agent-assisted authorization mechanism. Within the Internet of Vehicles, Contribution 17 unveils an efficient and secure blockchain consensus protocol. This protocol integrates a trust assessment mechanism, node partition strategy, and Dynamic Unique Node List to significantly enhance reliability and adaptability within IoV environments. Furthermore, it presents dynamic K-medoid practical byzantine fault tolerance—an optimized practical byzantine fault tolerance algorithm—designed to minimize latency and boost throughput efficiency.
In terms of energy efficiency and privacy preservation, Contribution 18 combines a federated learning framework with a pulsed neural network and differential privacy techniques to significantly reduce energy consumption and enhance privacy preservation for large-scale cross-device learning. Regarding blockchain-based privacy protection [30], Contribution 19 introduces privacy-preserving rewritable blockchain. This innovative approach supports flexible policies through an enhanced identity encryption scheme, batch policy support, and improved accountability traceability via a proxy re-encryption mechanism. For privacy protection within mobile crowdsensing applications, Contribution 20 presents PRBFM—a privacy-preserving blockchain framework grounded in thresholded linear secret sharing. By optimizing privacy protection and efficiency through sophisticated policy matching and task delegation mechanisms, this framework sets a new standard in secure data management.
In addressing the challenge of task allocation in spatial crowdsourcing [31], Contribution 21 introduces PKGS—a privacy-preserving hitchhiking task assignment scheme. Leveraging the Paillier cryptosystem, PKGS enables secure computation over encrypted data to safeguard the privacy of both workers and task locations. This innovative approach incorporates a privacy-preserving travel distance calculation protocol and a privacy-preserving comparison protocol to facilitate efficient task assignment while maintaining strict confidentiality. Within the domain of federated learning, Contribution 22 unveils FRIMFL—a fair and reliable incentive mechanism rooted in reverse auctions and reputation trust. Designed to ensure robust client participation and equitable reward distribution, this mechanism incentivizes truthful bidding through integrated reverse auctions. Additionally, it employs a trust-based reputation system to assess the quality of contributions. The proposed method features a weighted trust assessment technique for reliability measurement and utilizes the Shapley value method to allocate rewards fairly based on marginal contributions. Simultaneously, Contribution 23 presents EPTA-T, an advanced task assignment scheme that amalgamates temporal access control with attribute-based encryption. By harnessing an attribute-based encryption framework enhanced by function integration, EPTA-T supports granular and time-bound access control. This ensures effective protection of data privacy during task allocation and significantly enhances overall efficiency. Lastly, tackling the issue of privacy-preserving frequent itemset mining on cloud servers, Contribution 24 proposes PPFIM—a verifiable privacy-preserving frequent itemset mining protocol. Through a dual-cloud architecture and encrypted databases, PPFIM mitigates data leakage risks and fortifies system security, thereby providing a robust solution for preserving privacy in large-scale data analysis tasks.

Funding

This work is financially supported by the National Natural Science Foundation of China (Grant Nos. 62202051 and 62232002), the China Postdoctoral Science Foundation (Grant Nos. 2021M700435 and 2021TQ0042), the Open Project Funding of the Key Laboratory of Mobile Application Innovation and Governance Technology, the Ministry of Industry and Information Technology (Grant No. 20231FS080601-K), the Beijing Institute of Technology Research and Innovation Promoting Project (Grant No. 2023YCXY010), the Beijing Institute of Technology Research Fund Program for Young Scholars, and the Young Elite Scientists Sponsorship Program by CAST (Grant No. 2023QNRC001).

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

List of Contributions

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MDPI and ACS Style

Zhang, C.; Wu, T.; Zhang, W. Data Privacy and Cybersecurity in Mobile Crowdsensing. Electronics 2025, 14, 1038. https://doi.org/10.3390/electronics14051038

AMA Style

Zhang C, Wu T, Zhang W. Data Privacy and Cybersecurity in Mobile Crowdsensing. Electronics. 2025; 14(5):1038. https://doi.org/10.3390/electronics14051038

Chicago/Turabian Style

Zhang, Chuan, Tong Wu, and Weiting Zhang. 2025. "Data Privacy and Cybersecurity in Mobile Crowdsensing" Electronics 14, no. 5: 1038. https://doi.org/10.3390/electronics14051038

APA Style

Zhang, C., Wu, T., & Zhang, W. (2025). Data Privacy and Cybersecurity in Mobile Crowdsensing. Electronics, 14(5), 1038. https://doi.org/10.3390/electronics14051038

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