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.

Cloud technology has brought great convenience to enterprises as well as customers. System logs record notable events and are becoming valuable resources to track and investigate system status. Detecting anomaly from logs as fast as possible can improve the quality of service significantly. Although many machine learning algorithms (e.g., SVM, Logistic Regression) have high detection accuracy, we find that they assume data are clean and might have high training time. Facing these challenges, in this paper, we propose Robust Online Evolving Anomaly Detection (ROEAD) framework which adopts Robust Feature Extractor (RFE) to remove the effects of noise and Online Evolving Anomaly Detection (OEAD) to dynamic update parameters. We propose Online Evolving SVM (OES) algorithm as the example of online anomaly detection methods. We analyze the performance of OES in theory and prove the performance difference between OES and the best hypothesis tends to zero as time goes infinity. We compare the performance of ROEAD against state-of-the-art anomaly detection algorithms using public log datasets. The results demonstrate that ROEAD is able to remove the effects of noise and OES can improve the detection accuracy by more than 40%.

Cloud storage system provides facilitative file storage and sharing services for distributed clients. To address integrity, controllable outsourcing, and origin auditing concerns on outsourced files, we propose an identity-based data outsourcing (IBDO) scheme equipped with desirable features advantageous over existing proposals in securing outsourced data. First, our IBDO scheme allows a user to authorize dedicated proxies to upload data to the cloud storage server on her behalf, e.g., a company may authorize some employees to upload files to the company's cloud account in a controlled way. The proxies are identified and authorized with their recognizable identities, which eliminates complicated certificate management in usual secure distributed computing systems. Second, our IBDO scheme facilitates comprehensive auditing, i.e., our scheme not only permits regular integrity auditing as in existing schemes for securing outsourced data, but also allows to audit the information on data origin, type, and consistence of outsourced files. Security analysis and experimental evaluation indicate that our IBDO scheme provides strong security with desirable efficiency.

Due to the rapid development of demand response management and distributed energy resources, prosumers are becoming more proactive, which also promotes the emergence of peer-to-peer (P2P) energy trading mechanisms. However, we find that it is quite difficult to simultaneously achieve high computational efficiency, decentralized operations and solution optimality in a P2P energy trading mechanism, which is called the “P2P energy trading trilemma”. In this paper, we first propose a novel negotiation mechanism for P2P energy trading that can maximize social welfare in a decentralized manner and respect physical network constraints. Then, the local optimization problem is transformed into a closed-form alternating update algorithm (AUA), so that the computational efficiency can be greatly improved. Therefore, our proposed mechanism solves the “P2P energy trading trilemma” to a certain extent. Another challenge is that the locational marginal price (LMP)-based market mechanism cannot satisfy the incentive-compatible property. To encourage prosumers to cooperate in a lack-of-trust environment, a novel incentive-compatible mechanism is proposed for P2P energy trading using consensus mechanism inspired by proof of solution (PoSo) and smart contract (SC). Finally, we simulate the functionality of the mechanism in terms of convergence performance, reliability, scalability, computational efficiency, and SC operations.

Provable data possession (PDP) allows a user to outsource data with a guarantee that the integrity can be efficiently verified. Existing publicly verifiable PDP schemes require the user to perform expensive computations, such as modular exponentiations for processing data before outsourcing to the storage server, which is not desirable for weak users with limited computation resources. In this paper, we introduce and formalize an online/offline PDP (OOPDP) model, which divides the data processing procedure into offline and online phases. In OOPDP, most of the expensive computations for processing data are performed in the offline phase, and the online phase requires only lightweight computations like modular multiplications. We present a general OOPDP transformation framework which is applicable to PDP-related schemes with metadata aggregatability and public metadata expansibility. Following the framework, we present two efficient OOPDP instantiations. Technically, we present aggregatable vector Chemeleon hash functions which map a vector of values to a group element and play a central role in the OOPDP transformation. Theoretical and experimental analyses confirm that our technique is practical to speed-up PDP schemes.

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.