The connected vehicular ad-hoc network (VANET) and cloud computing technology allows entities in VANET to enjoy the advantageous storage and computing services offered by some cloud service provider. However, the advantages do not come free since their combination brings many new security and privacy requirements for VANET applications. In this article, we investigate the cloud-based road condition monitoring (RCoM) scenario, where the authority needs to monitor real-time road conditions with the help of a cloud server so that it could make sound responses to emergency cases timely. When some bad road condition is detected, e.g., some geologic hazard or accident happens, vehicles on site are able to report such information to a cloud server engaged by the authority. We focus on addressing three key issues in RCoM. First, the vehicles have to be authorized by some roadside unit before generating a road condition report in the domain and uploading it to the cloud server. Second, to guarantee the privacy against the cloud server, the road condition information should be reported in ciphertext format, which requires that the cloud server should be able to distinguish the reported data from different vehicles in ciphertext format for the same place without compromising their confidentiality. Third, the cloud server and authority should be able to validate the report source, i.e., to check whether the road conditions are reported by legitimate vehicles. To address these issues, we present an efficient RCoM scheme, analyze its efficiency theoretically, and demonstrate the practicality through experiments.

Smart grid can greatly improve the efficiency, reliability and sustainability of the traditional grids. In industrial smart grid, real-time user-side metering data may be frequently collected for monitoring and controlling electricity consumption. However, the procedure of frequently metering data collection may lead to sensitive information leakage. To address the security issues in industrial smart grid, we propose an efficient identity-based metering data aggregation scheme supporting batch verification by collector and ESP, respectively, which guarantees the privacy and integrity of metering data. In our scheme, collectors are allowed to collect and aggregate the metering data of users in their respective administrative domain without compromising the confidentiality of metering data. Security analysis demonstrates that our proposed scheme is provably secure in the random oracle and satisfies the above security requirements. Performance analysis indicates that our scheme outperforms existing solutions in terms of communication and computation costs.

In this note the authors give theL²(ⁿ) boundedness of a class of parametric Marcinkiewicz integralwith kernel function Ω inLlog⁺L(Sⁿ⁻¹) and radial functionh(|x|) ∈ l ∞ l(L^q)(+) for 1 < q ≦.As its corollary, theL^p(ⁿ)(2 < p < ∞) boundedness of andandwith Ω inLlog⁺L (S^n-1) and h(|x|) ∈ l^∞(L^q)(₊) are also obtained. Hereandare parametric Marcinkiewicz functions corresponding to the Littlewood-Paleyg*_λ-function and the Lusin area functionS, respectively.

Cross-silo federated learning (FL) allows organizations to collaboratively train machine learning (ML) models by sending their local gradients to a server for aggregation, without having to disclose their data. The main security issues in FL, that is, the privacy of the gradient and the trained model, and the correctness verification of the aggregated gradient, are gaining increasing attention from industry and academia. A popular approach to protect the privacy of the gradient and the trained model is for each client to mask their own gradients using additively homomorphic encryption (HE). However, this leads to significant computation and communication overheads. On the other hand, to verify the aggregated gradient, several verifiable FL protocols that require the server to provide a verifiable aggregated gradient were proposed. However, these verifiable FL protocols perform poorly in computation and communication. In this paper, we propose SVFL, an efficient protocol for cross-silo FL, that supports both secure gradient aggregation and verification. We first replace the heavy HE operations with a simple masking technique. Then, we design an efficient verification mechanism that achieves the correctness verification of the aggregated gradient. We evaluate the performance of SVFL and show, by complexity analysis and experimental evaluations, that its computation and communication overheads remain low even on large datasets, with a negligible accuracy loss (less than $1\%$ ). Furthermore, we conduct experimental comparisons between SVFL and other existing FL protocols to show that SVFL achieves significant efficiency improvements in both computation and communication.

With the development of information technology, massive and heterogeneous consumer electronic products can access the network. These products may engage third-party servers for federated learning and generating more accurate models, where they can monitor, collect and aggregate various data from households almost in real-time. Even though federated learning can update participant parameter data without collecting their raw data, prior research revealed that the shared gradients still retain sensitive information from the training set. Meanwhile, malicious third-party aggregation servers may return forged aggregated gradients, and lightweight execution of the entire solution needs to be ensured during the aggregation process. This paper demonstrates a verifiable federated learning scheme supporting secure data aggregation without using bilinear groups (FLVA) to address these issues. Particularly, to solve the issue of private key leakage in the gradient aggregation process on electronic product data, a three-party key negotiation protocol is developed. The private gradients are uploaded and aggregated in ciphertext format, which ensures the privacy of the electronic product gradient. Security analysis showed that our FLVA system can effectively protect the security and privacy of the private gradients. Finally, the experimental results showed that compared with existing solutions, the proposed scheme is more efficient and practical.