Edge computing paradigm deploys infrastructures on users' edge side, enabling data to be stored in edge storage systems. Thus edge computing finds a balance between remote cloud storage with high latency and local storage with limited resources. However, the frequent update of edge data increases the probability of data corrupting. To ensure availability and integrity of edge data without affecting future use, this article proposes a certificateLess edge recoverable data auditing (CL-ERDA) approach with corruption localization. First, the certificateless network coding signature in CL-ERDA is utilized to generate the homomorphic authenticator, addressing public key certificate management issues while avoiding insecure key escrow problems. Then, CL-ERDA designs a two-phase auditing method, where only file-level aggregation proofs are respond in first phase of external check and the second phase of self-check is launched only when damaged data is detected, reducing communication overhead of the auditing. Moreover, CL-ERDA combines hierarchical check method with batch localization method to achieve locating efficiently all the corrupted data at one time rather than locating only one corrupted data at once. Finally, security analysis shows that the proposed scheme resists various attacks and supports the unforgeability of authenticators. Experimental results demonstrate that CL-ERDA is efficient in auditing and corruption localization.

The smart grid leverages real-time data sharing to connect diverse stakeholders for efficient operations and precise service delivery. The transmitted data in smart grid is highly sensitive and vulnerable to eavesdropping and tampering. Deniable ring signature technology is a promising cryptographic primitive that addresses the aforementioned concerns by providing both anonymity and traceability. However, existing approaches present a fundamental trade-off in which the schemes based on the Public Key Infrastructure (PKI) are constrained by the prohibitively high overhead associated with certificate management. Identity-Based Cryptography (IBC) schemes eliminate certificates but are inherently susceptible to key escrow and require a centralized trust model. To address these issues, we propose ECDRS, an efficient certificateless deniable ring signature scheme with privacy preserving based on SM2 elliptic curve public key cryptography algorithm, designed to circumvent the certificate management overhead and mitigate the key escrow problem for secure communications in smart grids. Additionally, a batch verification algorithm is designed compatible with linear homomorphic additive aggregation, enabling efficient verification of multiple signatures. The security of ECDRS is formally proved under the random oracle model, including unforgeability, anonymity, traceability, and non-frameability. Performance analysis demonstrates that the proposed ECDRS achieves high efficiency in terms of computation and communication. For instance, when the number of ring members is set to n = 250, the total execution time for signature generation and verification is reduced by 50% to 97.01% compared to existing schemes. Furthermore, when the ring size n = 40 and the number of signatures eta = 200, the execution time for batch signature verification is reduced by 49.82% to 74.91%.

By leveraging the computational capabilities and proximity of edge nodes, federated learning has gained widespread adoption in edge computing, which enables multiple users to train models collaboratively without sharing their private data. However, sensitive information is still at risk of being inferred from gradients, unreliable communication may lead to single-point failures at aggregation nodes. To address these issues, propose VDBFL, a verifiable and privacy preserving federated learning scheme to resist potential Byzantine threats and data tampering in decentralized environment. Specifically, by integrating verifiable secret sharing and bilinear aggregate signatures, the proposed VDBFL employs a decentralized Byzantine-robust aggregation method to ensure the correctness and integrity of aggregated gradients. Moreover, a dynamic node management strategy for edge nodes and dropout tolerance for clients are introduced to enhance the scalability of system. Security analysis and experimental results show that VDBFL achieves high accuracy on public datasets. Compared to individual verification, the batch verification based on BLS aggregate signatures reduces verification overhead by 60%-70%.

As an emerging distributed machine learning paradigm, federated learning enables collaborative model training across multiple participants while safeguarding local data privacy. However, the development of federated learning on a central server introduces critical vulnerabilities, the single-point-of-failure risk. To address the current limitations of federated learning, this paper proposes an innovative practical Byzantine fault tolerance (RP-PBFT) consensus mechanism with reputation evaluation and proxy signature. Moreover, this paper proposed a comprehensive reputation evaluation model that dynamically assesses node trustworthiness through two critical dimensions, which one is the deviation between locally loaded gradients and global gradients, the other is the behavior of the nodes during the consensus process. Furthermore, the proposed mechanism integrates a (t, n) threshold proxy signature to delegate consensus participation rights for suspicious nodes, thereby improving the system’s effective fault tolerance under proxy-assisted consensus participation. Experimental results demonstrate that RP-PBFT reduces communication overhead by over 40

The Internet of Things (IoT) and edge computing have enabled the utilization of IoT for environmental monitoring and energy optimization. The Internet of Vehicles (IoV), with its extensive coverage and rich sensing capabilities, is particularly well-suited for these purposes, including traffic resource optimization. However, the openness of the IoV has introduced security and privacy concerns. Vulnerabilities in IoV systems could allow malicious adversaries to cause environmental contamination and to potentially compromise critical transportation infrastructure through targeted attacks. To address these issues, a solution based on secure multi-party computation (SMC) is proposed. First, an efficient anonymous authentication scheme is designed using linear equation system theory and secure multi-party computation, which prevents illegal vehicles from accessing the Green IoV. Second, a verifiable secure multi-party computation scheme is proposed based on replicated secret sharing, allowing vehicles to submit data to the Green IoV for environmental monitoring and resource optimization while maintaining privacy. Security analysis reveals that the proposed scheme possesses anonymity, verifiability, privacy preserving, and dynamic joining capabilities. Furthermore, experimental analysis demonstrates the proposed scheme achieving a runtime of only 3.7 ms each vehicle for authentication and the lightweight multi-party computation reduces once multiplication latency to 1.2 ms, 71% faster than similar methods.

Wireless sensor networks (WSNs) are widely used in environmental monitoring, industrial manufacturing, disaster relief, healthcare, and energy management. However, the development of WSNs still faces many challenges related to coverage and energy balancing among the distributed nodes. To address the above issues, we propose an adaptive learning Fruit Fly Optimization Algorithm (FOA) to optimize the nodes’ coverage and energy balancing in 2D and more complex 3D environments. Adaptive learning FOA incorporates a fusion of adaptive virtual force modeling and adaptive small habitat techniques to enhance initial search capabilities and maintain search balance in later stages. Moreover, we employ dynamic oppositional learning (DOL) and adaptive dimensional learning (ADL) to avoid falling into local optima and to improve search accuracy. Additionally, we introduce a real-time node energy consumption model, which calculates energy consumption during movement, coverage, and iteration of nodes. The proposed model enables continuous monitoring of node energy, helping to prevent energy loss and node failure, thereby enhancing the overall performance and stability of WSNs. The simulation results demonstrate the effectiveness of our approach: in the 2D scenario, the adaptive learning FOA achieves a maximum coverage rate of 94.86% and an average coverage rate of 94.18%, while in the 3D scenario, it reaches a maximum coverage rate of 97.68% and an average coverage rate of 96.32%. These results highlight the significant improvements in coverage and energy balancing, confirming the potential of our method to optimize WSN performance in diverse environments.

Efficient power management in smart grids relies on regularly obtaining the electricity usage of each user. However, the aggregation of the electricity usage data may expose user privacy. At present, most existing solutions aggregate the electricity data of the entire user set, which cannot meet the fine-grained requirements of the control center. Therefore, this paper proposes a verifiable privacy-preserving multisubset dynamic billing data aggregation scheme. Firstly, we divide the electricity data into k consecutive subsets and set dynamic pricing rules, such as pricing within a certain range as q1 and exceeding it as q2. Then, the smart meter encrypts the data using the Paillier cryptographic system with the corresponding subset of parameters and uploads them to the aggregation equipment. After the aggregation equipment completes the users’ data aggregation, the ciphertext C is sent to the control center. This process enables the control center to check data integrity and obtain the total number of people, electricity consumption, and costs for different ranges of electricity in a period of time at once, without the need to obtain data for individual users. Analysis and experiments show that this scheme can resist attacks from powerful adversaries on the transmission channel and has practicality and effectiveness.

Real-time analytics in Industrial Internet-of-Things (IIoT) has received remarkable attention recently due to its capacity to prevent downtime and manage risks. However, the sensed data in IIoT is considered private. Thus, the sensed data of IIoT nodes cannot be transmitted and utilized directly in the cloud server due to the risk of privacy leakage. Data aggregation can effectively balance the availability of data with privacy concerns, making it particularly well-suited for IIoT systems. Although several privacy-preserving aggregation schemes in IIoT have been proposed, the majority of them can only support a single type of aggregation that limits the application scenarios of data aggregation. To address the problems mentioned above, a real-time aggregation analysis scheme for IIoT is proposed, which is constructed based on secure multi-party computation with secret sharing. Specifically, the multi-party computation with secret sharing is utilized to implement data aggregation process for IIoT that achieves multiple types of data aggregation. In addition, the secret sharing is utilized in the proposed scheme that can significantly improve the efficiency of the proposed scheme compared with similar schemes. Moreover, the proposed scheme does not require the involvement of a trusted authority in the data aggregation. Security and performance analyses show that the proposed scheme can enhance the security of the sensed data while effectively aggregating data for real-time analytics in IIoT.

Vehicular ad-hoc networks (VANETs) enable communication among vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) with a dedicated short range communications (DSRC). In VANETs, the real-time trajectory prediction service can be provided by roadside units (RSUs) using data collected from vehicles. This service is important for the prevention of traffic accidents. Due to the openness of the communication in VANETs, the transmitted data is vulnerable to illegal users' attacks that results in the leakage of vehicles' data. To address the above concerns, a privacy-preserving multi-party computation scheme with secret sharing is proposed for vehicle trajectory prediction in VANETs. The proposed scheme is designed to protect the privacy of transmitted vehicle data during trajectory prediction. Note that four distributed interactive protocols of $\bm {\mathit {SecLog(\cdot)}}$, $\bm {\mathit {SecExp (\cdot)}}$, $\bm {\mathit {SecRec(\cdot)}}$ and $\bm {\mathit {SecCmp(\cdot)}}$ are introduced to carry out the computation of nonlinear logarithm, exponent, reciprocal and comparison functions in the proposed scheme. The aforementioned interactive protocols facilitate the distributed deployment of the Adaboost ensemble learning algorithm among multiple RSUs, while ensuring the privacy of vehicle data using secret sharing simultaneously. Security analysis proves that the proposed scheme can effectively protect the privacy of vehicle data. Performance analysis demonstrates that the proposed scheme has a minimal impact on the prediction accuracy of the Adaboost algorithm compared to the differential privacy (DP)-based scheme. Furthermore, the low latency of the proposed scheme allows for efficient trajectory prediction, as demonstrated by the runtime of the proposed scheme on test set.

Android is an open -source mobile operating system, with more than 70% of the mobile market share, widely popular on various intelligent devices. At the same time, the number of new malicious applications keeps increasing every year. In this paper, we first discuss the advantages and disadvantages of various detection methods for malicious software. A single detection method can only cover specific types of malware. Therefore, we propose a system that combines static structural analysis and dynamic detection of malware. This system has dual detection capability, which consists of a client and a server. The client is a lightweight Android application that is used to obtain the relevant data information of the installation package. The server is responsible for static analysis of APK and dynamic running of monitoring logs to get the relevant feature information. Based on the feature information, the Bagging algorithm of ensemble learning is adopted, and the decision tree and random forest are combined to identify the malware accurately. We collected 4210 Android software samples, with malicious apps accounting for about 20% of the total. Cross -testing of malware detection on this sample set showed that DroidExaminer achieved approximately 96% accuracy in detecting malware. It can resist confusion and conversion techniques, and the test performance overhead is less. In addition, DroidExaminer can alert the user to the details of malware intrusion so that the user can prevent malware intrusion.

A human-centered, sustainable development technological concept from industry 5.0 is rapidly sparking extensive discussions in the academic and professional communities. Massive data need to be outsourced to nearby fog nodes and remote cloud servers due to the constrained resource of terminals in industrial Internet of things (IIoT) in future industry 5.0. However, terminals are skeptical about the credibility of the outsourced data due to the loss of physical data possession. Existing data storage auditing schemes in cloud computing will be performed with high latency, which is not suitable for cloud-fog-assisted IIoT. To improve the computational efficiency and reduce the communication overhead, in this paper, a privacy-preserving dynamic auditing for regenerating code-based storage in cloud-fog-assisted IIoT is proposed. First, a generalized framework of exact reparation regenerating code is employed to encode the data file, which can improve the data storage security in cloud-fog-assisted IIoT. Then, the ZSS signature is used to generate the authentication tag for each encoded data segment. Moreover, a proper data structure is designed to store the encoded data and authenticators in storage servers based on the properties of exact reparation regenerating code, which can efficiently make full use of the fragmented storage space and significantly reduce the latency of data update. Security analysis shows that the proposed scheme provides the resistance of forgery attacks, replacing attacks, replaying attacks and ensures the data privacy. Performance analysis demonstrates that the proposed scheme can be performed with low computational cost and communication overhead that can be well used in cloud-fog-assisted IIoT.

In edge computing, the idle resources of the devices in the network can be virtualized into a platform that provides clients with storage resource and computing capability. Note that the service response of edge computing is faster than that of cloud computing. The service provision speed and the distributed resources utilization rate of edge computing will be further improved when integrated with 5G and software definition paradigm in the design of the network system. However, the issues of data storage security and edge devices’ trustworthiness seriously restrict the development of edge computing. To enhance the security of the data storage in edge computing, a secure distributed data integrity auditing is proposed. The proposed auditing scheme in this paper can be used to guarantee the correctness and the completeness of the stored data in 5G-enabled software-defined edge computing. The auditing results of the distributed data in the proposed scheme can be used as an important basis for evaluating the trustworthiness of the edge devices. Due to the utilization of certificateless cryptography in the design of the proposed scheme, the computational cost of the terminal side can be highly reduced. Security analysis of the proposed scheme demonstrates that the properties of key exposure resistance and privacy-preserving are provided in data auditing. Simulation results of the time cost of the server side and the terminal side show that the proposed scheme is highly efficient compared to previous schemes.

As a new generation of electricity system, smart grid significantly improves electricity services’ efficiency, reliability, and sustainability. The smart meters, which are the essential terminals, help establish two-way communication between users and electricity providers. While enjoying the convenience of smart meters, users face many challenges. On the one hand, malicious adversaries could attack the smart meters and thus steal the users’ privacy. On the other hand, the computational overhead of electricity data verification is high for lightweight smart meters. To address above issues, a lightweight authentication and group key management scheme is proposed. In the proposed scheme, the physical properties of the Physical Unclonable Function (PUF) are exploited to defend against external attacks from adversaries. Moreover, the Chinese Remainder Theorem (CRT) is used to broadcast the updated group keys for the legitimate smart meters in the community. In addition, the aggregated signature is utilized to reduce the overhead of the data verification. Finally, the Random Oracle Model (ROM) is used to demonstrate that the proposed scheme meets many security requirements. Performance analysis shows that the proposed scheme is more suitable for smart grid compared to previous schemes.

Vehicular networking is a communication platform that integrates the computing power of vehicles, roadside units, and infrastructures, which is capable of offering services to terminals characterized by low latency, high bandwidth, and reliability. Artificial intelligence (AI) has been developed rapidly over the past few years, and numerous AI applications requiring high computing power in vehicular networking have emerged (e.g., automatic driving, collision avoidance, and trajectory prediction). However, the computation of the AI model requires high computing power, and the vehicles on the road have low computation capability, which significantly hinder the development of intelligent transportation based on AI in vehicular networking. In this article, a distributed computatin offloading scheme is developed, which can be used to outsource the tasks of the AI model computation to nearby vehicles and roadside units in vehicular networking. To reduce the computational burden and decrease the latency of the computation on the vehicle side, the optimized genetic algorithm is adopted to divide the computation of the sigmoid function into multiple sub-tasks. Moreover, secure multi-party computation and homomorphic encryption are applied in the sub-task computation to enhance the security of the AI model computation in vehicular networking. As indicated by the security analysis, the proposed scheme can be proved to support privacy preservation in the multi-party computation of the AI model. As revealed by the simulation results, the proposed scheme can be performed with low computational time with different lengths of keys and transmitted parameters in practice.

Internet of things (IoT) has emerged as a promising technology that can be widely used in various industries to realize real-time information collection, so as to improve production efficiency and reduce running costs. By combining the technology of IoT, maritime transportation systems (MTS) can prevent vessels collision, improve the efficiency of maritime transportation and reduce the loss of revenue for ports and shipbuilders. The large amount of real-time data generated in IoT-enabled MTS can be efficiently utilized to predict the future trajectories and hotspots of vessels on the sea combined with historical data. However, the maritime traffic data in MTS cannot be effectively processed in traditional big data analysis methods, and the integrity of it needs to be checked before being used to achieve the prediction of trajectories and high-density areas of vessels. In this paper, we propose a flexible data integrity checking scheme with original data recovery in IoT-enabled MTS. In the proposed scheme, the data blocks of vessels are encoded based on the technology of erasure coding. To ensure the availability of the historical data, the existence and the integrity of the data elements stored in the cloud can be checked. Moreover, the original data blocks can be recovered efficiently if the encoded data elements have been corrupted or deleted. Security analysis demonstrates that the proposed scheme can be proved to be correct and is secure against malicious attacks. Performance analysis shows that our scheme is more efficient than the previous schemes.

由于计算机类课程涵盖了计算机软硬件各方面内容,并涉及数学领域基础知识,因此课程内容之间的联系性较低.为了提高计算机类专业学生对课程内容的接受度,尤其是针对知识储备较少的低年级同学,本文针对计算机类课程教学方法创新进行探索,通过引入"5E"教学模式提升学生对课程内容的主动学习兴趣.同时,在教学过程中积极融入"对分课堂",提高学生的课堂参与度,进而提升计算机类课程的实际教学效果.

Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a new dendrite neuron model (DNM), which combines metaheuristic algorithm and dendrite neuron model effectively. Eight learning algorithms including traditional backpropagation, classic evolutionary algorithms such as biogeography-based optimization, particle swarm optimization, genetic algorithm, population-based incremental learning, competitive swarm optimization, differential evolution, and state-of-the-art jSO algorithm are used for training of dendritic neuron model. The optimal combination of user-defined parameters of model has been systemically investigated, and four different datasets involving classification problem are investigated using proposed DNM. Compared with common machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and artificial neural networks, dendritic neuron model trained by biogeography-based optimization has significant advantages. It has the characteristics of simple structure and low cost and can be used as a neuron model to solve practical problems with a high precision.

Internet of things (IoT) is a technology that can collect the data sensed by the devices for the further real-time services. Using the technique of cloud computing to assist IoT devices in data storing can eliminate the disadvantage of the constrained local storage and computing capability. However, the complex network environment makes cloud servers vulnerable to attacks, and adversaries pretend to be legal IoT clients trying to access the cloud server. Hence, it is necessary to provide a mechanism of mutual authentication for the cloud system to enhance the storage security. In this paper, a secure mutual authentication is proposed for cloud-assisted IoT. Note that the technique of chameleon hash signature is used to construct the authentication. Moreover, the proposed scheme can provide storage checking with the assist of a fully-trusted entity, which highly improves the checking fairness and efficiency. Security analysis proves that the proposed scheme in this paper is correct. Performance analysis demonstrates that the proposed scheme can be performed with high efficiency.

The technique of Internet of things (IoT) connects the distributed devices via the network that can realize smart applications, such as intelligent transportation, intelligent manufacturing, smart grid and smart home. Edge computing integrates the edge devices together and provides efficient computation and storage services with low latency for users. By combining with blockchain, edge computing can provide more secure data storage and transmission supporting tamper resistance and traceability for IoT. However, the existing data storage schemes are not suitable for processing the gathered data in blockchain enabled edge computing and the efficiency of the data error locating in the previous schemes is very low. In this paper, a secure distributed data storage scheme is proposed, which can be used in blockchain enabled edge computing. In the design of the proposed scheme, the technologies of bilinear pairing and BLS-HLA (BLS-Homomorphic Linear Authenticator) are used, which allows end clients to check the uploaded data storage. In addition, the mechanisms of error locating and data dynamics are supported due to the utilization of CBF (Counting Bloom Filter). Security analysis result indicates that the proposed scheme is correct, data blocks detectable and false positive rate negligible. Performance analysis result shows that the proposed scheme can be executed with low computational cost, which is practical for IOT environment in blockchain enabled edge computing.

Currently, VANETs are becoming more and more popular and have been applied in many different applications. However, because the feature of VANETs’ topologies is relatively stable, the routing protocol designed for VANET can be more efficient from a macro perspective. Specifically, the concept of incompletely predictable networks (IPNs), where nodes travel around the basic positions, can be used to model stable VANETs, as the ones related to taxis during our daily lives. In this paper, we propose a new protocol, named Greedy Probability-based Routing Protocol, for rather stable VANETs. A new concept named “anti-pheromone” is put forward to achieve high energy efficiency. Simulation results illustrate that GPRP has proper utilization ratio of nodes to achieve energy efficiency and has a stable performance when network size changes.