Federated learning (FL) has emerged as a promising approach for collaborative machine learning while respecting data privacy. However, directly aggregating local models during FL training can expose sensitive information, as adversaries may analyze model parameters to infer the underlying raw data. To address this concern, implementing a robust identity authentication mechanism can verify the integrity of local models and confirm their sources. However, many existing authentication schemes are computationally intensive, which can hinder performance. In this article, we present a lightweight authentication scheme for FL called EPPAFL. This innovative approach is compatible with all machine learning algorithms, maintains data utility, and ensures strong privacy protection. Additionally, it can be integrated with detection mechanisms to defend against data poisoning and free-rider attacks. Specifically, we first design a certificateless signature scheme based on the elliptic curve cryptography (ECC) and then propose an anonymous and efficient authentication protocol based on the Chinese remainder theorem (CRT), which supports batch verification, dynamic updates, and revocations of group keys. Moreover, participants interact anonymously to achieve secure communication, and trusted authority (TA) can track and recover the real identities of participants in case of malicious activities. We provide formal security proofs and analyses to demonstrate the achieved security properties. Performance evaluations demonstrate that our proposed protocol significantly reduces the costs associated with signature generation and verification, outperforming the existing schemes in both communication and computational efficiency.

Driven by Web 3.0, the number of decentralized applications (DAPPs) continues to increase, and the need for decentralized authentication across applications becomes more and more significant. In addition, multi-dimensional reputation assessment becomes especially critical in a decentralized environment. However, there is still a lack of trusted and flexible self-sovereign identity (SSI) authentication mechanisms in Web 3.0 decentralized zero-trust cross-applications access scenarios. In response to these challenges, we propose a decentralized authentication scheme that fuses reputation assessment and attribute signature for the open and self-organized Web3 application. Firstly, we design an attribute signature method based on the attributes in a user’s verifiable credentials (VCs). The signature can be used for authentication across Web3 applications, and the authenticator can flexibly adjust the attributes to be verified and their number according to specific needs. Meanwhile, in the attribute signature, we construct a multi-dimensional reputation assessment mechanism to comprehensively assess the user’s reputation from multiple aspects to determine his/her security risk level, so as to reduce the possibility of identity fraud. Ultimately, through comparative analysis, we verify the advantages of the scheme in terms of privacy, scalability and attack resistance. Moreover, we analyze the performance of the key algorithms in the scheme through simulation experiments, and the experimental results show that our scheme is feasible.

Multi-replica storage is an advanced extension of traditional cloud storage that allows data owners to customize the number of backups for file blocks based on their relative importance. In such settings, remote auditing mechanisms are essential for verifying data integrity and ensuring that the cloud service provider (CSP) maintains the pre-negotiated number of replicas. However, existing schemes often expose block positions and backup quantities to the CSP, making users' data vulnerable to template attacks. Meanwhile, secure deduplication significantly reduces storage overhead and user costs while preserving data confidentiality. In this paper, we propose a novel multi-replica cloud storage scheme that, for the first time, simultaneously supports cross-user deduplication and integrity auditing in the ciphertext domain. The proposed scheme can not only protect data privacy from template attacks but also enable the elimination of redundant ciphertext replicas and audit authentication tags across users at the block level. Formal analysis validates the correctness and security guarantees of our scheme. Experimental results demonstrate its effectiveness with modest overhead.

Blockchain-based IoT applications in agriculture have drawn extensive attention in recent years, allowing the implementation of smart agriculture solutions. By transmitting collected relevant data to a control center through the blockchain, corresponding regulation can be realized in agricultural production management systems. However, existing efforts for directly adopting the technique to data transmission are obstructed by several issues. The traceability of agricultural data stored in the blockchain leads to the exposure of the identity of the data collecting devices. And the tracing difficulty of completely invisible data for identity protection also exists in the smart agricultural system. To tackle these limitations, we propose a novel blockchain-based smart agricultural IoT system framework for regulating the agricultural production environment through trusted data. First, the elliptic curve integrated encryption scheme (ECIES) and the group signature scheme are integrated to guarantee the traceability and identity protection of the data and equipment, respectively. Then, to enhance the security of session key transmission in the ECIES scheme, we further design an on-chain-off-chain key agreement protocol (ECIES-OOKA). In addition, we propose a novel group manager selection method based on probabilistic linguistic term sets (PLTSs) for the group signature implementation. Finally, a practical example is provided to demonstrate the group manager selection process and verify the feasibility of the proposed method. The security and performance analysis for the system framework are also presented.

In Industrial Internet, mutual authentication between enterprises is a prerequisite for establishing reliable upstream and downstream relationships. Existing authentication methods suffer from complicated certificate management and key escrow problems. Moreover, many authentication mechanisms cannot resist common security attacks and have high computational overhead and communication costs. Therefore, this paper proposes a blockchain-based certificate-free cross-domain authentication mechanism for Industrial Internet. By establishing an Ethereum consortium blockchain as the trusted cornerstone among different regions, industrial enterprises in each region generate the user’s private key with the key generation center in the region, thus avoiding the key escrow problem. This consortium blockchain adopts the proof of authority consensus mechanism for scalability and throughput. Industrial enterprises in different regions invoke smart contracts and query other industrial enterprises for mutual authentication and key negotiation. SVO logic proves the proposed scheme achieves the intended authentication goal, and the automated formal verification tool Scyther proves the scheme’s security. In addition, compared with seven related schemes in the last three years, the experimental results show that the proposed scheme has low communication overhead and computational cost in the authentication key negotiation phase. The experiments on the Ethereum consortium blockchain built by Raspberry Pi prove the effectiveness of the proposed scheme. Finally, the comparative analysis of common security properties proves the reliability of the scheme.

An industrial network connects devices, sensors, and other physical devices through wireless networks to collect, share, and analyze data to increase productivity, reduce costs, and improve product quality. Due to the accessibility of wireless channels to everyone, the number of malicious exploits of these services is increasing. Thus, confirming the validity of smart devices and servers and guaranteeing communication security are essential. Mutual authentication has become an essential security concern in wireless communication to meet this tendency. However, available techniques rely on prime factorization and discrete logarithm problems that cannot effectively resist quantum attacks. This study presents an anonymous authentication system based on edge computing for the Industrial Internet of Things (IIoT). Specifically, we devised an RLWE-based authentication method to withstand diverse attacks while improving operating efficiency. The proposed scheme achieves mutual authentication in a single-message exchange round, while ensuring user anonymity and perfect forward security. Security analysis and verification with ProVerif demonstrate that our scheme enhances privacy and reliability. Furthermore, experimental results show that our scheme outperforming existing schemes in terms of communication and computing costs.

The emergence of edge computing makes it possible to realize new technologies, such as virtual reality and augmented reality. However, a large number of devices and more messages at the edge bring more security problems. Therefore, it is an important research topic to provide users with faster network services while ensuring confidentiality and authentication of data transmission. Because signcryption can encrypt and sign messages at the same time, it has become a new cryptographic primitive. In the meantime, certificateless signcryption guarantees data confidentiality and authentication and addresses traditional single point failure problems based on the trust center and the problem of relying on a trusted third party. Therefore, certificateless signcryption has attracted great attention from academia and industry. But certificateless signcryption also faces two types of attacks. In order to more effectively resist these two types of attacks, we propose a certificateless signcryption mechanism based on blockchain. This mechanism can make good use of the nontamperable feature of blockchain, prevent illegal users from substituting the public key of the user, and guarantee signature nonrepudiation. And our scheme is investigated in a comparative study with eight schemes. Comparative analysis outcomes demonstrate our scheme has achieved better results in efficiency and security. The process of signcryption and unsigncryption consumes the least amount of computation, which is very suitable for the edge computing environment.

As the Industrial Internet of Things (IIoT) continues to grow in scale, edge devices will generate massive amounts of data every single day. However, most of the IIoT data exists in the form of data silos, which makes it difficult to share data across domains securely. Therefore, a secured data-sharing scheme for IIoT based on blockchain and federated learning (FL) is proposed in this article. Leveraging blockchain in FL systems to enhance the tamper-proof and decentralized capabilities of IIoT devices. Model parameter validation and incentives are also added to the consensus algorithm to encourage more IIoT data owners to contribute local privacy data and arithmetic power. To address potential security issues, such as parameter leakage and inference attacks in data sharing, this article designs an adaptive differential privacy mechanism and a node contribution consensus mechanism. Without affecting the global model’s accuracy, some of the noise is also reduced. The reputation mechanism is used to resist poisoning attacks by malicious nodes. It is demonstrated on different data sets that our scheme has high global model accuracy and can effectively resist 30% model poisoning attacks.

As a distributed ledger technology, blockchain has excellent openness and transparency, which can provide data security management services for distributed intelligent systems and establish effective security guarantee mechanisms. However, precisely due to the open nature of blockchain, malicious users can trace the real transaction transfer path with high probability and even obtain the real identity of users by collecting transaction information on the blockchain and performing data analysis. Besides, existing intelligent systems lack effective encryption or desensitization measures, and attackers are able to access data in intelligent systems through identity forgery and other means, posing a huge potential risk to user privacy. To alleviate the above security issues, this paper proposes an efficient blockchain-based privacy-preserving scheme with attribute and homomorphic encryption, which can not only achieve user-level fine-grained secure access control but also supports the transmission and verification of blockchain data in the form of ciphertext. The access control method based on attribute-based encryption and the data transmission method based on homomorphic encryption are proposed, and the blockchain-based access whitelist mechanism is designed to reduce the resource loss due to repeated authentication. Simulation calculations and blockchain performance experiments show that this scheme can develop flexible access policies according to the attributes of users, has good performance in computational efficiency, and the blockchain performance test results are stable with errors in milliseconds for all operations. It has certain application potential in the field of distributed intelligent systems and blockchain.

Social networks are full of spams and spammers. Although social network platforms have established a variety of strategies to prevent the spread of spam, strict information review mechanism has given birth to smarter spammers who disguise spam as text sent by ordinary users. In response to this, this paper proposes a spam detection method powered by the self-attention Bi-LSTM neural network model combined with ALBERT, a lightweight word vector model of BERT. We take advantage of ALBERT to transform social network text into word vectors and then input them to the Bi-LSTM layer. After feature extraction and combined with the information focus of the self-attention layer, the final feature vector is obtained. Finally, SoftMax classifier performs classification to obtain the result. We verify the excellence of the model with accuracy, precision, F1-score, etc. The results show that the model has better performance than others.

United Nations security council resolutions are voted on by fifteen members of the security council. A resolution will be deemed to be adopted if more than nine of the member states adopt it and all five permanent members do not use the veto. The five permanent members can vote yes, no, and abstain against a resolution. The vague set can be explained intuitively with the voting model, indicating the three conditions for voting. Which are favor, against, and abstention. When the agent nodes were selected by the consensus mechanism of delegated proof of stake (DPoS), the voting node can only vote support instead of against and abstention, and there are no penalties for malicious nodes. Besides, there is no clear method to select the agent node if the votes obtained by the last agent node are the same as the votes cast by others. This article puts forward a kind of vague set to improve DPoS consensus mechanism. Just like how human elections are held, this method allows each node to vote for the agent node. This is a fair and effective way of selecting agent node when alternate nodes are of the same value, it improves the security and fairness of blockchain, reduces the probability that malicious nodes will be selected as agent nodes. We prove that the maximum probability of nodes after the vote of the fuzzy membership degree is 0.5. The feasibility and effectiveness of the improved consensus mechanism is verified by examples.

Online product review is becoming one of important reference indicators for people shopping, but the current product review site contains a lot of fraudulent reviews. Group review spamming, which involves a group of fraudulent reviewers writing a lot of fraudulent reviews for one or more target products, becomes the main form of review spamming. However, solutions for group spammer detection are very limited, and due to lack of ground-truth review data, this problem has never been completely solved. In this paper, we propose a novel three-step method to detect group spammers based on Clique Percolation Method (CPM) in a completely unsupervised way, called GSCPM. First, it utilizes clues from behavioral data (timestamp, rating) and relational data (network) to construct a suspicious reviewer graph. Then, it breaks the whole suspicious reviewer graph into k-clique clusters based on CPM, and we consider such k-clique clusters as highly suspicious candidate group spammers. Finally, it ranks candidate groups by group-based and individual-based spam indicators. We use three real-world review datasets from Yelp.com to verify the performance of our proposed method. Experimental results show that our proposed method outperforms four compared methods in terms of prediction precision.

The exponential growth of devices connected to the network has resulted in the development of new Internet of Things (IoT) applications and online services, which may have diverse and dynamic requirements on received quality. Although, the emerging software-defined networking (SDN) approach can be leveraged for the IoT environment, to dynamically achieve differentiated quality levels for different IoT tasks in very heterogeneous wireless networking scenarios, the open interfaces in SDN introduces new network attacks, which may make SDN-based IoT malfunctioned. The challenges lies in securely using SDN for IoT systems. To address this challenge, we design a SDN-based data transfer security model middlebox-guard (M-G). M-G aims at reducing network latency, and properly manage dataflow to ensure the network run safely. First, according to different security policies, middleboxes related to the defined secure policies, are placed at the most appropriate locations, using dataflow abstraction and a heuristic algorithm. Next, to avoid any middlebox becoming a hotspot, an offline integer linear program (ILP) pruning algorithm is proposed in M-G, to tackle switch volume constraints. In addition, an online linear program (LP) formulation is come up to handle load balance. Finally, secure mechanisms are proposed to handle different attacks. And network routing is solved flexibly, through dataflow management protocol, which are formulated via combining tunnels and tags. Experimental results demonstrate that this model can improve security performance and manage dataflow effectively in SDN-based IoT system.

The prosperity of social networks provides users with convenient communication but also attracts a large number of spammers. To solve this problem, this paper combines supervised learning and unsupervised learning algorithms, and proposes a novel hybrid model based on OPTICS and SVM. First, we collected a dataset from Sina Weibo including 10,000 users and 134,188 messages; then extracted the content based features and user behavior based features from the dataset; afterwards, we applied the features into the hybrid model to establish the classification model. The experiment shows that the proposed approach is capable of detecting spammers effectively with 87.6% spammers and 94.7% legitimate users correctly classified.

A social spammer detection model based on tri-training (SSDTT) is adopted. The main procedure of the work is: First, train three original classifiers with a small amount of labeled data. Then, select confident users that are labeled for a classifier if the other two classifiers agree on the labeling as new training data. Afterwards, repeat these steps until three classifiers are not updated. Experimental results indicate that SSDTT has the same performance with the supervised learning in the case of lacking sufficient labeled data.

As an important branch of wireless sensor networks, wireless body area networks (WBAN) has attracted widespread attention in various fields because of its portability and mobility. However, because much of the data collected by WBAN are related to personal information of the user, the sensitive private data may be at risk of leakage or malicious modification in the actual process of application and deployment. In order to assure the security and privacy of user's data in the environment of WBAN, this paper presents a Data Privacy Protective Mechanism for WBAN. In order to secure data and secure transmission, this mechanism combines symmetric key with an asymmetric key to transmit user's data. Then, it cuts and reorganizes the data in the process of transmission to better capture defense and the attacks of the nodes. Ultimately transmits the user's data securely under the condition that the data collected by the nodes are confidential and secure. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract The cloud storage service has been widely used in daily life because of its convenience. However, the service frequently suffers confidentiality problems. To address this problem, some efforts have been made on keyword search over encrypted data schemes. For instance, the chaotic‐based keyword search scheme over encrypted data has been proposed recently. However, the scheme just only support single‐ keyword search each time, which severely limits its utilization in cloud storage. This article proposes a novel chaotic‐based time‐aware multi‐keyword search scheme with designated server. Inner product similarity is adopted in our scheme to realize multiple keyword search and remove the constraint of single‐keyword search each time. Timed‐release encryption is integrated into the proposed scheme at the same time, which enables the data sender to specify the time when the cloud servers can search the encrypted data. Analysis indicates that our scheme not only can counter off‐line guessing attacks to the ciphertext and trapdoor, but also supports ranked search with a reasonable computational cost. Copyright © 2015 John Wiley & Sons, Ltd.