Machine learning (ML) techniques have been widely used in many smart city sectors, where a huge amount of data is gathered from various IoT devices. As a typical ML model, support vector machine (SVM) enables efficient data classification and thereby finds its applications in real-world scenarios such as disease diagnosis and anomaly detection. Training an SVM classifier usually requires a collection of labelled IoT data from multiple entities, raising great concerns about data privacy. Most of the existing solutions rely on an implicit assumption that the training data can be reliably collected from multiple data providers, which is often not the case in reality. To bridge the gap between ideal assumptions and realistic constraints, in this paper, we propose secureSVM, which is a privacy-preserving SVM training scheme over blockchain-based encrypted IoT data. We utilize the blockchain techniques to build a secure and reliable data sharing platform among multiple data providers, where IoT data is encrypted and then recorded on a distributed ledger. We design secure building blocks, such as secure polynomial multiplication and secure comparison, by employing a homomorphic cryptosystem, Paillier, and construct a secure SVM training algorithm, which requires only two interactions in a single iteration, with no need for a trusted third-party. Rigorous security analysis prove that the proposed scheme ensures the confidentiality of the sensitive data for each data provider as well as the SVM model parameters for data analysts. Extensive experiments demonstrates the efficiency of the proposed scheme.

Industrial Internet of Things (IIoT) is considered as one of the most promising revolutionary technologies to prompt smart manufacturing and increase productivity. With manufacturing being more complicated and sophisticated, an entire manufacturing process usually involves several different administrative IoT domains (e.g., factories). Devices from different domains collaborate on the same task, which raises great security and privacy concerns about device-to-device communications. Existing authentication approaches may result in heavy key management overhead or rely on a trusted third party. Thus, security and privacy issues during communication remain unsolved but imperative. In this paper, we present an efficient blockchain-assisted secure device authentication mechanism BASA for cross-domain IIoT. Specifically, consortium blockchain is introduced to construct trust among different domains. Identity-based signature (IBS) is exploited during the authentication process. To preserve the privacy of devices, we design an identity management mechanism, which can realize that devices being authenticated remain anonymous. Besides, session keys between two parties are negotiated, which can secure the subsequent communications. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed mechanism.

Decentralized Applications (DApps) are increasingly developed and deployed on blockchain platforms such as Ethereum. DApp fingerprinting can identify users' visits to specific DApps by analyzing the resulting network traffic, revealing much sensitive information about the users, such as their real identities, financial conditions and religious or political preferences. DApps deployed on the same platform usually adopt the same communication interface and similar traffic encryption settings, making the resulting traffic less discriminative. Existing encrypted traffic classification methods either require hand-crafted and fine-tuning features or suffer from low accuracy. It remains a challenging task to conduct DApp fingerprinting in an accurate and efficient way. In this paper, we present GraphDApp, a novel DApp fingerprinting method using Graph Neural Networks (GNNs). We propose a graph structure named Traffic Interaction Graph (TIG) as an information-rich representation of encrypted DApp flows, which implicitly reserves multiple dimensional features in bidirectional client-server interactions. Using TIG, we turn DApp fingerprinting into a graph classification problem and design a powerful GNN-based classifier. We collect real-world traffic datasets from 1,300 DApps with more than 169,000 flows. The experimental results show that GraphDApp is superior to the other state-of-the-art methods in terms of classification accuracy in both closed- and open-world scenarios. In addition, GraphDApp maintains its high accuracy when being applied to the traditional mobile application classification.

The blooming trend of smart grid deployment is engaged by the evolution of the network technology, as the connected environment offers various alternatives for electrical data collections. Having diverse data sharing/transfer means is deemed an important aspect in enabling intelligent controls/governance in smart grid. However, security and privacy concerns also are introduced while flexible communication services are provided, such as energy depletion and infrastructure mapping attacks. This work proposes a model Permissioned Blockchain Edge Model for Smart Grid Network (PBEM-SGN) to address the two significant issues in smart grid, privacy protections and energy security, by means of combining blockchain and edge computing techniques. We use group signatures and covert channel authorization techniques to guarantee users’ validity. An optimal security-aware strategy is constructed by smart contracts running on the blockchain. Our experiments have evaluated the effectiveness of the proposed approach.

Blockchain technology has been deemed to be an ideal choice for strengthening existing computing systems in varied manners. As one of the network-enabled technologies, cloud computing has been broadly adopted in the industry through numerous cloud service models. Fusing blockchain technology with existing cloud systems has a great potential in both functionality/ performance enhancement and security/ privacy improvement. The question remains on how blockchain technology inserts into current deployed cloud solutions and enables the reengineering of cloud datacenter. This survey addresses this issue and investigates recent efforts in the technical fusion of blockchain and clouds. Three technical dimensions roughly are covered in this work. First, we concern the service model and review an emerging cloud-relevant blockchain service model, Blockchain-as-a-Service (BaaS); second, security is considered a key technical dimension in this work and both access control and searchable encryption schemes are assessed; finally, we examine the performance of cloud datacenter with supports/ participance of blockchain from hardware and software perspectives. Main findings of this survey will be theoretical supports for future reference of blockchain-enabled reengineering of cloud datacenter.