With the rapid development of the Internet of Vehicles (IoV), the need for reliable real-time communication and secure data sharing has become increasingly critical. However, existing solutions struggle with high computational demands and limited vehicle processing capability. While attribute-based encryption enables fine-grained data sharing, its high decryption overhead requires outsourcing to Roadside Units (RSUs), which introduces reliability risks such as ciphertext decryption errors, threatening system integrity. To address these issues, we propose a smart contract-based data sharing framework with parallel outsourced computing in IoV. Unlike most existing outsourced ABE (OABE) schemes that rely on serial decryption and lack robust parallel verification, and blockchain-integrated approaches that focus on general trust without tailored parallelism, our system uniquely combines MapReduce-based parallel decryption (dividing RSU tasks into distributed Map and Reduce stages) with a ciphertext verification mechanism (achieving 99.95% error detection with under 2% communication overhead) and smart contracts for auto-auditing (2 blocks, 0.003 ETH cost, preventing 98.7% malicious behaviors). Blockchain integration ensures service quality guarantees by providing accountability and ensuring that computation results are auditable, with responsible parties held accountable. Experiments and SUMO simulations show that our system maintains a decryption time of nearly 1 second with 100 attributes, improving by 1.6 seconds over serial decryption, while reducing transformation key generation complexity fromO(n) toO(1). These results confirm that our system achieves efficient data sharing with computational correctness and security in IoV.

Efficient power management in smart grid relies on collecting fine-grained power consumption data from users. However, these data may reveal sensitive information about individuals' habits and lifestyles. Various multidimensional data aggregation schemes leveraging public key encryption (PKE) algorithms have been proposed to address this problem. Never theless, most of these schemes come with significant performance costs. In addition, if the secret key of a smart meter was leaked, the confidentiality of encrypted user power data could be at risk. In this article, we propose a lightweight, multidimensional, and privacy-preserving data aggregation scheme with fault-tolerance and key-leakage resilience for smart grid without relying on a trusted third party (TTP), named FKLM-PDA, in which a novel data packaging method that transforms users' multidimensional data into a one-dimensional format is designed, enabling data center parse aggregated results in each dimension, reducing computation and communication costs. For better efficiency, an effective encryption algorithm is proposed to replace the expensive additive homomorphic PKE, like the Paillier cryptosystem, which combines a random masking with secret-sharing based key separation, ensuring threshold key-leakage resilience under a bounded, non-colluding leakage model. Furthermore, not only does FKLM-PDA enhance the fault tolerance mechanism of data transmission from smart meters to a corresponding fog node, but also it supports dynamic user management for joining and exiting improving scalability. Security analysis confirms that FKLM-PDA is privacy-preserving and secure while guaranteeing key-leakage resilience, fault tolerance, authentication, and data integrity. Through performance evaluations, FKLM-PDA outper forms the existing schemes and is superior in computation and functional in communication.

Vehicle platoon offers numerous benefits in terms of road safety, energy efficiency, and traffic management in urban freight delivery. Privacy preservation is critical here: location information ties to customer confidentiality and reputation guarantees platoon reliability, yet most existing platoon management schemes fail to preserve privacy while achieving vehicle location-matching. Meanwhile, traditional distance calculation methods such as Euclidean distance are unsuitable for urban road layouts, and most schemes assume member vehicles must follow to unified endpoints, a rigid constraint conflicting with the scenario's needs. In this paper, we propose a privacy-preserving platoon management scheme for flexible platoon splitting in urban freight delivery (PPFPS). In detail, the PPFPS scheme leverages location and reputation to achieve flexible platoon splitting in platoon management while preserving vehicle privacy. Specially, we design an encrypted Manhattan distance calculation method (EMC) by combining bloom filters and Paillier cryptosystem, which is tailored to the road layouts in urban environments and deployed on cloud servers. The EMC method enables privacy-preserving location matching to achieve flexible platoon splitting, and reputation is used to ensure the reliability of vehicle platoon. Furthermore, the EMC method significantly minimizes the involvement of the trusted authority by introducing cloud-assisted approaches. Theoretical analysis demonstrates that the PPFPS scheme effectively preserves privacy and defends a variety of potential attacks. Simulation evaluation confirms that the PPFPS scheme supports more functions while significantly reducing computation overheads by 66.59% to 78.72% on the TA side, and maintains communication overheads of the similar order of magnitude as the existing schemes.

Abstract Precise forecasting of photovoltaic (PV) power generation upholds flexibility and reliability within the power grid. Due to the data security dilemma of previous forecasting methods, federated learning (FL) has been widely studied for its ability to train models without sharing training data. However, the incorrect behavior from untrusted devices and servers in traditional FL frameworks can undermine the integrity of the global model, precipitating inaccurate power generation forecasting. Therefore, we propose a blockchain-enabled trusted Byzantine-robust FL framework, called TBFL, designed for decentralized and privacy-preserving PV power generation forecasting. Specifically, this framework features a trusted supervision mechanism, which can effectively eliminate malicious gradients to achieve a high-quality model. In addition, a multilevel differential privacy scheme is designed to strike a balance between privacy protection and model accuracy. Finally, a model clipping algorithm based on neuronal similarity is implemented to optimize both the duration and consumption associated with local device training. Comprehensive experimental outcomes demonstrate that the framework TBFL can successfully improve robustness, and achieve similar efficiency as FedAvg while maintaining a high forecasting accuracy.

Federated learning (FL) prevents direct exposure of raw data. However, it remains vulnerable to privacy and security threats such as inference and poisoning attacks. Traditional differential privacy (DP) methods utilize noise injection to mitigate these attacks, which inherently degrades the accuracy of the model. In this paper, we propose a robust FL framework with two alternative effective defense mechanisms to enhance privacy preservation for various scenarios. We first propose a dual-layer client-server collaborative differential privacy (CLDP). Clients utilize adaptive local differential privacy (LDP) for data privacy, while the server uses central differential privacy (CDP) on the global model to mitigate poisoning attacks. Second, we propose enhanced central differential privacy (ECDP), a layer-specific protection mechanism that strategically injects targeted noise into non-batch normalization layers to further preserve data privacy. To mitigate noise-induced model performance degradation, our solution combines personalized data transformation and gradient sparsification, effectively alleviating both non-IID data distribution skew and cumulative noise effects. Architecturally, we decentralize the federated learning system through edge node integration, thereby eradicating single points of failure. Experimental results demonstrate that our framework achieves a superior accuracy-privacy trade-off under strict privacy constraints, providing robust protection without compromising practical utility.

Vehicular Sensor Networks (VSNs) are integral to intelligent transportation systems, enabling real-time communication and collaborative sensing among vehicles. However, their open wireless communication environment presents significant challenges in terms of data security and privacy protection. Although various certificateless aggregate signcryption (CLASC) schemes have been proposed to address these issues, many still suffer from security vulnerabilities and performance inefficiencies. We conduct a detailed security analysis of a recently proposed CLASC scheme by Dai et al., highlighting its susceptibility to public key replacement attacks. To demonstrate this vulnerability, we construct a specific attack algorithm and, based on our findings, propose a security-enhanced CLASC scheme. The proposed solution integrates blockchain technology to improve system decentralization, enhances resistance to collusion attacks, and supports malicious identity revocation mechanism. We provide formal security proofs under standard cryptographic hardness assumptions and evaluate the performance of the scheme through both theoretical analysis and experimental validation. The results show that our approach significantly strengthens security while maintaining high efficiency, making it well-suited for secure and scalable communication in vehicular sensor network (VSN) environments.

While the collection of users’ live or periodic electricity consumption data brings significant advantages for the operation of smart grids, it also heightens the risk of user privacy leakage. Numerous data aggregation schemes have been proposed to address this issue. However, most of these schemes either fail to accommodate the need for multifunctional data analysis or rely on a trusted third party (TTP). Given the efficient data processing capabilities offered by fog computing, we propose a blockchain-based privacy-preserving data aggregation (BPRM) scheme supporting multifunctionality for fog-assisted smart grid without TTP. This scheme ensures data confidentiality and data integrity while providing various statistical functions. In addition, we implement a consensus mechanism between smart meters, further enhancing the security and robustness of the smart grid system. Moreover, not only does the proposed the batch verification reduce the authentication costs but also support error detection in signatures. With BPRM, data center can calculate multiple statistical functions, achieving a win-win strategy. Extensive security and performance analyses demonstrate that BPRM can withstand various security threats and effectively protect user privacy while maintaining efficiency in both computational and communication overhead.

Vehicular ad hoc networks (VANETs) are essential technologies to ensure safe road traffic management and enhance driving convenience. Nowadays, diversified authentication schemes have been developed in VANETs for the purpose of safer communication between nodes. For instance, biometric technology which employs biometric information as users’ authentic identity is widely adopted in message authentication due to its visible benefits. Nonetheless, there is a significant problem in current biometric identity-based authentication schemes that noise is inevitable in each collection of biometric information, making these schemes lack critical error tolerance. Additionally, anonymous biometric identity is difficult to be realized, which fails to meet the basic standard of VANETs. For solving the above key issues, we propose the first efficient fuzzy certificateless signature-based (FCLS) authentication scheme using anonymous biometric identities for VANETs. In virtue of its superior error tolerance, it enables authentication between two identities represented by two attribute sets within a certain Hamming distance. Besides, the newly developed authentication scheme realizes effective conditional privacy so that drivers’ real biometric identities can be ensured. Through the formal security proof, this FCLS scheme is existentially unforgeable against adaptive chosen message attack (EU-CMA) in the random oracle model (ROM), which reaches the higher security. Compared with current advanced schemes, the new authentication scheme is more efficient in computation and communication according to performance analysis.

Abstract Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores the probability distribution of the DNN output layer to filter out the unknown classes and further designs a cost-sensitive matrix to address the class imbalance problem. Additionally, we propose the utilization of the Harris Hawk optimization algorithm to modify the model parameters and improve its performance. The experiments are validated on two different datasets, and the results demonstrate that CM-UTC not only outperforms existing methods in terms of overall performance but also exhibits superior capability in correctly identifying samples from the minority class.

Abstract With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes.

Privacy preservation in federated learning (FL) has received considerable attention and many approaches have been proposed. However, these approaches rendered the uploaded gradients invisible to the server, which poses a significant challenge in defending against poisoning attacks. In poisoning attacks, malicious or compromised participants use poisoned training data or forged local updates to disrupt the training process. It is hard for cloud servers to defend against poisoning attacks due to the invisibility of gradients. To address this issue, we propose a privacy-preserving FL scheme (PFLS) against poisoning attacks to eliminate the impact of model poisoning attacks while protecting the privacy of participants. Specifically, a dynamic adaptive defense mechanism is designed to mitigate the impact of malicious gradients and locate malicious participants. To protect participants’ privacy, a multidimensional homomorphic encryption method is constructed with a hierarchical aggregation architecture. The security analysis illustrates that the PFLS scheme can ensure the privacy of FL participants. The experimental results demonstrate that a high-detection rate of malicious participants and a balance between efficiency and robustness are achieved.

Vehicular ad-hoc networks (VANETs) can help facilitate traffic flow, reduce accidents, and enhance the driving experience. However, VANETs have some problems in terms of the authenticity and integrity of transmitted information and the preservation of vehicles’ privacy. Many certificateless aggregate signature (CLAS) schemes have been proposed to address these concerns. Nevertheless, most of these schemes suffer from security and efficiency challenges, such as the inability to resist forgery attacks and high computation costs. Recently, an efficient CLAS scheme with conditional privacy protection has been put forward by Chen et al. However, there is a security flaw in this scheme. In this paper, we give a specific attack algorithm to indicate that Chen et al.’s proposal cannot resist a public key replacement attack initiated by external adversaries and then put forward a security-enhanced scheme. Furthermore, an efficient invalid signature identification algorithm is designed to identify invalid signatures after an aggregate verification has failed. Through rigorous security analysis, it has been verified that the scheme put forward can satisfy the fundamental security requirements of VANETs. Compared with other related schemes, our proposal improves efficiency while providing privacy and security guarantees for VANETs.

Abstract Public key encryption with keyword search is a promising primitive which enables search over encrypted data in secure data outsourcing services. In traditional construction, the associated keywords may be recovered from a given trapdoor by a malicious server through keyword guessing attacks. Therefore, the notion of public-key authenticated encryption with keyword search (PAEKS) was introduced, where a sender encrypts (and authenticates) the keywords using a receiver’s public key and its secret key. In this paper, we consider the forward security for PAEKS and introduce a new primitive: forward secure public-key authenticated encryption with keyword search (FS-PAEKS), which captures the information leakage risk from previously issued queries due to the updates on the outsourced data. Technically, we embed a non-interactively agreed key into the cipher-keyword generation algorithm, and bind the cipher-keyword and the trapdoor with a set converted from algorithm-generation time. Finally, we present an efficient FS-PAEKS scheme supporting conjunctive query, and prove its forward security against chosen keyword attacks and keyword guessing attacks. To illustrate practical performance, we implement our FS-PAEKS and related PAEKS schemes based on Enron dataset in real cloud environment.

Attribute based encryption (ABE) is usually used for authorization to ensure data confidentiality because it can provide fine-grained access control. However, the data of smart grid is not completely divided, and there are many cases of collaborative access to data. The existing ABE based collaboration schemes have the defect that the ciphertext increases with the increase of attributes. Due to the limited storage and transmission capacity of smart grid equipment, the previous schemes are not suitable for smart grid. An attribute based collaborative access control scheme with constant ciphertext length for smart grid is proposed. By extending the collaborative nature to ABE with constant ciphertext length, the vacancy of collaborative ABE is made up. Performance analysis shows that this scheme is significantly better than the traditional collaborative ABE scheme in terms of storage and computing overhead. Security analysis shows that the scheme can ensure the confidentiality of data and support the revocation of collaborative at any time.

The emergence of edge computing brings data processing and storage to the vicinity of terminal equipment, which can quickly respond to user needs and reduce the computational burden of the traditional centralized cloud computing model, resulting in a model of edge computing-assisted cloud computing. In this architecture, how to prevent other untrusted entities from leaking user privacy has become one of the most critical concerns. To address this concern, many cryptographic schemes supporting the traditional cloud model to protect the data security sharing of IoT devices have been proposed. However, resource-constrained devices are an essential component of the Internet of Things (IoT). Its characteristics are one of the main reasons that affect the efficiency of schemes, and traditional cryptographic schemes are not suitable for edge computing. Therefore, in order to ensure secure data sharing between IoT devices, we come up with an improved certificateless online/offline signcryption (CLOOSC) scheme and achieve lower computational overhead, when offline calculation is not considered, the resource-constrained IoT device under the cloud-edge collaboration architecture requires only one point multiplication, while only one bilinear pairing is required in the verification phase. In the random oracle model, our scheme is proved to be IND-CCA2 secure. The experiment results show our scheme can be lightweight in terms of time cost.

In smart grids, a major challenge is how to effectively utilize consumers’ energy consumption data while preserving security and privacy. In this article, we tackle this challenging issue and focus on energy theft detection, which is very important for smart grids. Specifically, we note that most existing energy theft detection schemes are centralized, which may be unscalable, and more importantly, may be very difficult to protect data privacy. To address this issue, we propose a novel privacy-preserving federated learning framework for energy theft detection, namely, FedDetect. In our framework, we consider a federated learning system that consists of a data center (DC), a control center (CC), and multiple detection stations. In this system, each detection station (DTS) can only observe data from local consumers, which can use a local differential privacy (LDP) scheme to process their data to preserve privacy. To facilitate the training of the model, we design a secure protocol so that detection stations can send encrypted training parameters to the CC and the DC, which then use homomorphic encryption to calculate the aggregated parameters and return updated model parameters to detection stations. In our study, we prove the security of the proposed protocol with solid security analysis. To detect energy theft, we design a deep learning model based on the state-of-the-art temporal convolutional network (TCN). Finally, we conduct extensive data-driven experiments using a real-energy consumption data set. The experimental results demonstrate that the proposed federated learning framework can achieve high accuracy of detection with a smaller computation overhead.

Software-Defined Network (SDN) can improve the performance of the power communication network and better meet the control demand of the Smart Grid for its centralized management. Unfortunately, the SDN controller is vulnerable to many potential network attacks. The accurate detection of abnormal flow is especially important for the security and reliability of the Smart Grid. Prior works were designed based on traditional machine learning methods, such as Support Vector Machine and Naive Bayes. They are simple and shallow feature learning, with low accuracy for large and high-dimensional network flow. Recently, there have been several related works designed based on Long Short-Term Memory (LSTM), and they show excellent ability on network flow analysis. However, these methods cannot get the deep features from network flow, resulting in low accuracy. To address the above problems, we propose a Hybrid Convolutional Neural Network (HYBRID-CNN) method. Specifically, the HYBRID-CNN utilizes a Deep Neural Network (DNN) to effectively memorize global features by one-dimensional (1D) data and utilizes a CNN to generalize local features by two-dimensional (2D) data. Finally, the proposed method is evaluated by experiments on the datasets of UNSW_NB15 and KDDCup 99. The experimental results show that the HYBRID-CNN significantly outperforms existing methods in terms of accuracy and False Positive Rate (FPR), which successfully demonstrates that it can effectively detect abnormal flow in the SDN-based Smart Grid.

Summary Fog computing is composed of various computers with weak performance instead of servers with strong performance. As history has shown, there has not been a general pairing‐free certificateless signature scheme that is mainly designed with modular exponentiation and modular multiplication that can possess resistance to Type I and Type II adversaries. The lightweight certificateless signature algorithm with low requirements for computing and storage capabilities, which can be practicably implemented in fog computing, needs to be studied. Therefore, a new hard mathematic problem is firstly defined in this paper, which is called variant of RSA problem. Then, a new general pairing‐free certificateless signature scheme is proposed based on the variant of RSA problem and the discrete logarithm problem. Fortunately, the proposed scheme is the first RSA‐based certificateless signature scheme that can possess resistance to Type I and Type II adversaries. A formal security proof is provided to demonstrate that, under adaptively chosen message attacks, the scheme is provably secure against Type I and Type II adversaries in the random oracle model. When compared with other known pairing‐free certificateless signature schemes of the same type, the computation cost of our scheme is slightly higher; however, a higher security level can be achieved.

Abstract An aggregate signature refers to a signature, by which n signatures σ1,...,σn corresponding to n messages m1,...,mn and n users u1,...,un can be transformed into a single short signature . Besides, anyone can be convinced by the single short signature that the n messages m1,...,mn were definitely signed by the n users u1,...,un correspondingly. The concept of certificateless cryptography is proposed, so as to settle the key escrow problem in ID‐based cryptography and eliminate the demand for certificates in certified cryptography. A certificateless signature scheme was proposed by Chen et al. in 2014, which was extended into a certificateless aggregate signature scheme. In this paper, two attacks are firstly provided, so as to indicate that the certificateless signature scheme is insecure against a Type I adversary and a Type II adversary. And then, it is demonstrated that the certificateless aggregate signature scheme is not able to achieve the security levels they claimed due to the weaknesses of the certificateless signature scheme. Copyright © 2016 John Wiley & Sons, Ltd.

According to the inspirations from history, we introduce a new cryptography primitive called fuzzy certificateless signature, which not only eliminates the key escrow problem inherently existed in fuzzy identity-based signature but also possesses the error tolerance property of fuzzy identity-based signature that allows for a set of attributes ω to verify a signature produced with a private key for an identity ω′ if and only if the distance between the two identities ω and ω′ is within a certain threshold. In this paper, the concept of fuzzy certificateless signature is first proposed, and then, the syntax and security model of fuzzy certificateless signature are formally defined. In the next step, so far, the first concrete fuzzy certificateless signature scheme is proposed, which may be practicably implemented in biometric identification. In addition, a formal security proof is provided, so as to demonstrate that in the random oracle model, our newly proposed scheme is existentially unforgeable against Types I and II chosen message attacks formalized in the security model under the computational Diffie–Hellman assumption. Copyright © 2016 John Wiley & Sons, Ltd.