The proliferation of mobile smart devices with ever-improving sensing capacities means that Mobile Crowd Sensing (MCS) can economically provide a large-scale and flexible solution. However, existing MCSs face threats to privacy and fairness when recruiting workers due to information sensitivity, uncertainty about worker behavior, and budget constraints. To address the above issues, we propose an Anonymity, Trust, and Fairness in Privacy Protection (ATFPP) service construction framework to cost-effectively improve the quality of data at MCS. The main innovations are as follows: Firstly, on anonymity, in order to protect the privacy of workers, we propose a Privacy-Preserving (PP) framework based on an anonymous three-party platform, which realizes a full-process privacy-preserving scheme for workers. Second, on trust, we design more efficient Truth Discovery (TD) algorithm and adopt multifactor trust assessment method to identify more trustworthy workers. In addition, in terms of fairness, the fair distribution of compensation is realized through reasonable budget and approximate Shapley method. Finally, the proposed ATFPP scheme is theoretically proven to be correct and effective. Simulations based on real-world datasets illustrate that our ATFPP service construction scheme outperforms the state-of-the-art method in terms of both privacy protection and data quality.

Intelligent Transport Systems are designed to revolutionize the performance and efficiency of Vehicular Ad Hoc Networks (VANETs). Consumer electronics encompassing autonomous driving and route planning contribute to a better driving experience. Federated learning enables vehicle collaboration to train global models of intelligent transport without sharing local data for personalized consumer electronics. However, due to the dynamic network topology and unreliable open channels of VANETs, various potential risks undermine the credibility of establishing intermediate model parameters. To address these issues, we propose a lightweight authentication and privacy-preserving aggregation scheme for blockchain-enabled federated learning in VANETs(LPBFL). Specifically, we first construct a distributed secure authentication framework for blockchain federated learning that facilitates seamless authentication of mobile vehicles and security of model transmission. Subsequently, we design a lightweight three-party authentication key agreement based on chaotic map that establishes a session key for secure transmission of the intermediate model. In addition, we propose a continuous authentication adaptive model aggregation algorithms to ensure local model integrity while improving the quality of global models. Finally, security analyses and proofs show that LPBFL enhances the privacy and reliability of intermediate model parameters. Comprehensive experimental evaluations substantiate that the proposed LPBFL facilitates lightweight authentication while maintaining superior model accuracy.

The dramatically growing trend of vehicles equipped with driving camera recorders has allowed realizing real-time crowdsourced video sharing in vehicular edge computing (VEC). Such cameras can assist in monitoring objects directly in front of and behind the vehicles, enabling them to provide important visual information through real-time video streaming in case of possible accidents. Exploiting the on-board units (OBUs) for VEC can allow drivers and passengers to share and access on-road video surveillance services. However, data security and privacy concerns of video generators (owners) are two key challenges that can severely limit video sharing in a VEC environment. In this article, we propose a blockchain empowered publish/subscribe (P/S) scheme to enable one-to-many secure video sharing in the VEC scenario. Then, we design an attribute-based encryption algorithm with static and dynamic attributes (ABE-SD) to achieve fine-grained access control in a mobile environment. Finally, We utilize permissioned blockchain and smart contracts to record access policy and publish and subscribe events, thus resulting in user self-certification and event traceability. The numerical results indicate that our proposed scheme ABE-SD outperforms traditional centralized CP-ABE methods in terms of encryption and decryption performance. The simulation experiments demonstrated that the proposed video-sharing scheme is secure and efficient.

Early warning of student performance is using data analytics to predict future performance and intervene in advance. The popularity of machine learning has improved prediction accuracy. However, most current models only consider subjective student factors without examining the external environment and objective elements. Meanwhile, the global pandemic of Pneumonia has brought serious disruptions to teaching and learning in universities, and existing models cannot cope with this challenge. In this study, we propose a neural network model that integrates various internal and external factors and incorporates data-level sample synthesis and multi-classification cost-sensitive learning methods to achieve early warning of student performance in universities and improve teaching quality and management. Experimental results show that the model can be applied to teaching scenarios with a mixture of online and offline teaching, has higher accuracy than previous prediction mechanisms that only consider some student’s academic characteristics, and outperforms traditional machine learning methods.