The maturity in information technology and new energy technologies enables participants to generate, buy, and sell energy in energy trading systems. Although applying blockchain technology to energy trading has solved some drawbacks in traditional centralized energy systems, the openness and transparency characteristics make the trading records stored on the blockchain vulnerable to data-mining attacks that may cause indispensable privacy leakage. Due to high efficiency and low overhead, noise-addition is an appropriate solution for privacy preservation. Nonetheless, recent research on noise-addition needs to generate massive accounts, which brings a certain amount of waste and inconvenience for later regulation and management. To avoid the aforementioned issues, this paper proposes a consortium blockchain-enabled scheme to ensure the privacy of data stored on the blockchain and resist linking attacks initiated by data mining algorithms. Our scheme utilizes a dynamic partition algorithm to leverage an account mapping algorithm and a virtual token algorithm. Specifically, the account mapping algorithm utilizes a dynamic account allocation method to hide the trading distribution of active users. Furthermore, the virtual token algorithm applies Laplace noise to hide the actual energy consumption of inactive users and curb excessive accounts generation. Finally, we formally demonstrate the privacy and effectiveness of our proposed scheme in security analysis and experiment evaluations.
As Internet-of-Things (IoT) service becomes richer, data sharing among different IoT systems gets popular. The traditional IoT system provides data storage and access service with the central cloud, which faces serious trust and security challenges. To provide a cross-system data sharing service, we adopt blockchain to build a multicenter data management (DM) framework and construct a trustable environment for data sharing. As regards to a security problem, attribute-based encryption (ABE) has been applied to the IoT system, but it still relies on the central server. Therefore, we design an ABE algorithm that could be used for multicenter scenario and shift DM to blockchain instead of a central server. Moreover, IoT devices always cannot afford complex encrypt computations as they have limited computing resource. To solve this, we design an obfuscating policy to shift encryption computations to the cloud instead of terminals. In this way, IoT devices could encrypt data with low computation cost. Security analysis and simulations prove that the algorithm we designed could reduce computation burdens of IoT terminals in data encryption and decryption phases effectively and safely.
Information plays a significant role in modern process industries due to the demand for flexibility and mobility in production. Therefore, it is important to preserve the privacy and security of the information exchanged and shared between partners in value chains, for instance asset chains or supply chains. Since many companies have their own technological solutions and methods for operation and control, this information is regarded as their intellectual property. For the protection of recipes, material flows, or operational and control variables during the manufacturing of products, various methods are available. These techniques include anonymization and encryption solutions. To deal with mathematical models and the computation of formulas, homomorphic encryption schemes can be applied to the data which have to be shared within a value chain. Based on the experience of previous implementations of homomorphic crypto system in different domains, the opportunities for adaptation of encryption methods in process industries are considered. This paper proposes the application of homomorphic encryption in a value chain and defines a specific protocol that enables the Paillier cryptosystem on a time series. A use case is designed for confidential information exchange between a secret owner and a value provider in a value chain. The architecture of confidentiality-preserving information sharing satisfies the zero-knowledge proof requirement and shows low similarity between original and recovered messages.
In recent decades, intelligent transportation systems (ITS) have improved drivers' safety and have shared information (such as traffic congestion and accidents) in a very efficient way. However, the privacy of vehicles and the security of event information is a major concern. The problem of secure sharing of event information without compromising the trusted third party (TTP) and data storage is the main issue in ITS. Blockchain technologies can resolve this problem. A work has been published on blockchain-based protocol for secure sharing of events and authentication of vehicles. This protocol addresses the issue of the safe storing of event information. However, authentication of vehicles solely depends on the cloud server. As a result, their scheme utilizes the notion of partially decentralized architecture. This paper proposes a novel decentralized architecture for the vehicular ad-hoc network (VANET) without the cloud server. This work also presents a protocol for securing event information and vehicle authentication using the blockchain mechanism. In this protocol, the registered user accesses the event information securely from the interplanetary file system (IPFS). We incorporate the IPFS, along with blockchain, to store the information in a fully distributed manner. The proposed protocol is compared with the state-of-the-art. The comparison provides desirable security at a reasonable cost. The evaluation of the proposed smart contract in terms of cost (GAS) is also discussed.
With the widespread of 5G networks, the application of Federated Learning (FL) in Internet of Things (IoT) has become a trend. However, the trust problem caused by the centralized aggregation server, and the inefficiency problem caused by the low-performance devices, are still key challenges. Several studies involving asynchronous FL have been conducted to accelerate the training process, but they usually have a decreased model performance. In this paper, a blockchain-based asynchronous federated learning framework with a dynamic scaling factor is proposed. By adopting the blockchain, the trust problem among devices can be addressed. Meanwhile, the novel dynamic scaling factor is proposed to help improve the FL efficiency and accuracy. Extensive experiments are conducted on heterogeneous devices and the results show that the proposed framework mitigates the impact of low-performance devices while being as efficient as traditional FL with the extra benefit of alleviating the trust problem among IoT devices.
The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.
With the development of the Internet of Things and the demand for telemedicine, the smart healthcare system has attracted much attention in recent years. As a platform for medical data interaction, the smart healthcare system is demanded to ensure the privacy of both the receiver and the sender, as well as the security of data transmission. In this paper, we propose a privacy-preserving data transmission scheme where both secure ciphertext conversion and malicious users identification are supported. In particular, the OT m n protocol is introduced to guarantee the two-way privacy of communication parties. Meanwhile, we adopt proxy reencryption algorithm to support secure ciphertext conversion so as to ensure the confidentiality of data in many-to-many communication pattern. In addition, by taking advantage of the concept of blockchain technology, a novel OT m n protocol is proposed to prevent data from being tampered with and effectively identify malicious users. Theoretical and experimental analyses indicate that the proposed scheme is practical for smart healthcare with high security and efficiency.
Hanlin Zhang, Peng Gao, Jia Yu, Jie Lin ยท 5 authors
Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work, and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.
Chang Liu, Shaoyong Guo, Song Guo, Yong Yan ยท 6 authors
With the development of smart cities, the chimney construction method can no longer meet service needs. It is extremely urgent to build a unified urban brain, and the core issue is data sharing and fusion. Aiming at the problems of data island, data leakage, and high trust cost in the IoT of the smart city, a lightweight and trusted sharing mechanism (LTSM) is proposed. First, the blockchain is combined with federated learning to realize the data sharing, which not only protects the private data, but also ensures the sharing process trust. Then, a node selection algorithm based on credit value and a node evaluation algorithm based on smart contract are designed to improve the quality of federated learning. Finally, we propose an improved raft consensus to meet the delay and security requirements of the consortium blockchain in the smart city scenario. In the simulation, we evaluate the federated learning algorithm, the node selection algorithm, and the improved raft consensus, respectively. The experimental results show that the LTSM mechanism has a good application value. The federated learning model has a better accuracy, but its training time is also longer. The node selection algorithm is helpful to improve the accuracy of the federated learning model. The improved raft consensus improves the throughput.
Blockchain technology holds several promises for many application areas; however, it is not without its limitations. One of the most significant weaknesses of blockchain technology is its substantial energy consumption. Many researchers have proposed solutions to reduce the energy demands of this technology - such as the use of alternative consensus algorithms and the use of renewable energy. However, the use of alternative trust and reputation models to improve sustainability (by, for instance, selecting miners based on these trust or reputation values) has not been widely investigated. In this paper, we propose a reputation model that quantifies and compares the trustworthiness of miners based on their behaviours within a blockchain network. The model is evaluated analytically and compared to other trust and reputation models for miners. The evaluation shows that our model fulfils several desirable properties that should always be satisfied by reputation models, whereas other models do not always meet these requirements. In addition, we perform experimental evaluations to represent the performance of our model and its accuracy in detecting malicious miners. We also report the effectiveness of using the model in reducing the energy consumption of blockchain-based systems.
Alexander Djamali, Patrick Dossow, Michael Hinterstocker, Benjamin Schellinger ยท 7 authors
Abstract Due to a steeply growing number of energy assets, the increasingly decentralized and segmented energy sector fuels the potential for new digital use cases. In this paper, we focus our attention on the application field of asset logging, which addresses the collection, documentation, and usage of relevant asset data for direct or later verification. We identified a number of promising use cases that so far have not been implemented; supposedly due to the lack of a suitable technical infrastructure. Besides the high degree of complexity associated with various stakeholders and the diversity of assets involved, the main challenge we found in asset logging use cases is to guarantee the tamper-resistance and integrity of the stored data while meeting scalability, addressing cost requirements, and protecting sensitive data. Against this backdrop, we present a blockchain-based platform and argue that it can meet all identified requirements. Our proposed technical solution hierarchically aggregates data in Merkle trees and leverages Merkle proofs for the efficient and privacy-preserving verification of data integrity, thereby ensuring scalability even for highly frequent data logging. By connecting all stakeholders and assets involved on the platform through bilateral and authenticated communication channels and adding a blockchain as a shared foundation of trust, we implement a wide range of asset logging use cases and provide the basis for leveraging platform effects in future use cases that build on verifiable data. Along with the technical aspects of our solution, we discuss the challenges of its practical implementation in the energy sector and the next steps for testing in a regulatory sandbox approach.
Nowadays, mission-critical applications need delay-free responses, which can be achieved by computation at the edge devices known as edge computing. But edge computing needs cloud computing services to perform massive intelligent tasks like AI-based prediction and analysis, which still possesses high latency in making intelligent decisions. This can be resolved by bringing intelligence at the edge device or edge server, which introduces complex problems either at the proximity of edge devices, that is, consumer electronic devices (CED), or at the CED by bringing intelligence at the edge. The process of bringing intelligence to the edge is called edge intelligence (EI). The computation at edge servers is susceptible to various security and privacy issues and possesses high latency due to data propagation from the device to the dedicated edge server. To overcome the aforementioned issues, this study presents a blockchain-based edge intelligence system to ensure the CEDs' data security, privacy, latency, and efficiency. The proposed system uses public and private blockchains to fulfil the gaps mentioned above of the traditional systems. The public blockchain ensures CEDs' data communication privacy security, whereas private block-chain ensures secure communication among the EI servers. Then, we present the use case scenario of blockchain and edge intelligence (EI) in the COVID-19 pandemic and evaluate its performance over computation cost by comparing the intelligence at CED with the intelligence at the edge server and centralized cloud server (CCS).
The anonymity of blockchain identities brings security risks to transactions. To solve this problem, this paper proposes a trusted identity model based on smart contracts and introduces the CA certificate of the PKI system to endorse the authenticity of the user's identity. The model structure is designed, the blockchain digital certificate format is given, the cross-domain authentication scheme based on smart contracts is described, and the security and efficiency of the model are analyzed. In terms of security, the model meets the needs of entity verification; in terms of efficiency, the use of a hash algorithm to construct a complete certificate chain, compared with existing solutions, significantly improves the efficiency of cross-domain authentication.
Domenico Lattuca, Luca Di Mauro, Francesco Bisconti, Federico Civerchia ยท 8 authors
Connected and autonomous vehicles run their control algorithms in dedicated on-board computing platforms, which will become obsolete long before the end of the life cycle of the vehicles, severely limiting the evolution of their control software and the deployment of cooperative vehicular applications. A promising solution for this problem is to delegate the most demanding computational tasks to the edge nodes the of Vehicular Ad-hoc Networks, leveraging on the Vehicular Edge Computing paradigm. This requires both low-latency, high-bandwidth communications and secure computing offloading. In this paper, we propose an architecture that ensures supporting secure computation offloading, using the IOTA-VPKI security scheme, without additional delay overhead. Furthermore, to demonstrate the applicability of the proposed scheme to a real case, we measured the time required for the execution of a maneuver plan supervised by an application, the Maneuver Control (MC), located on an edge node. Experimental results show that the described scheme is a very promising solution.
The rapid growth of Internet of Things (IoT) applications poses a great challenge to the computation capability of smart mobile equipments (SMEs). Fog Computing, as a promising technology, provides fast computing services for resource-limited SMEs in various applications. In this paper, we consider a blockchain-based fog computing network consisting of SMEs, fog nodes (FNs) and the cloud server. To optimize the delay and energy consumption of processing computation-intensive tasks, two offloading models are introduced, namely, task offloading to the device-to-device (D2D) cooperation group and to a nearby FN. Additionally, the blockchain technology is enabled to prevent malicious nodes from modifying with transaction information by maintaining a continuous tamper-proof ledger database. To reduce the delay and energy consumption of the traditional consensus mechanism, we propose the voting-based delegated proof of stake consensus mechanism, in which the FNs with the top half of votes will form a verification set, and the FNs will take turns being the manager to generate new blocks. Furthermore, to minimize the network cost, we jointly optimize task offloading decision, transmission rata allocation and computing resource allocation under various constraints. Finally, the effectiveness of the proposed scheme is demonstrated.
Chenchen Tan, Xinghao Li, Tom H. Luan, Bruce Gu ยท 6 authors
With the evolving Internet of Vehicles (IoVs), the onboard resources of vehicles in computing and communication are experiencing fast growth. The sharing of road information and computing results among vehicles in proximity can effectively improve the utility of IoVs. However, remote inter-vehicular resource sharing, e.g., information and computing resource sharing, remains an under-explored issue. Motivated by this, we propose a novel digital twin based fair trading platform built upon consortium blockchain to enable city-wide vehicular resource sharing. Specifically, we first develop a digital twin based vehicular platform to enable vehicular resource sharing in the cloud. To track and secure the resource sharing among digital twins, the consortium blockchain is deployed, which is enforced by the designed smart contracts with an efficient Proof-of-Stake (PoS) consensus algorithm. In addition, an innovative incentive mechanism is devised to motivate the city-wide resource sharing for vehicles, which can maximize the profits of task publishers. Using extensive evaluations, we show the effectiveness of the proposed system.
Ying Chen, Tianhui Zhou, Jun Zhou, Zhenfu Cao ยท 6 authors
Self-organizing vehicular social networks underpin many location-based services (LBS) such as those that collect and share environmental information (e.g., traffic and weather conditions) among vehicular users and the infrastructure. There are, however, security and privacy considerations in the sharing of such information, and one popular approach is to design lightweight authentication solutions for LBS. Existing approaches may suffer from limitations such as significant computational and/or storage overheads, latency and time delays, and consequently impractical for resource-constrained on-board units. In this paper, we propose an efficient privacy-preserving LBS bundle authentication scheme (hereafter referred to as SAVE) through secure redundancy filtering in self-organizing vehicular social networks. Firstly, an enhanced self-healing key distribution protocol with distributed revocation is proposed to reduce communication cost for retransmitting lost key material and resist free-riding attacks to enhance the authentication efficiency. Then, based on it, a generalized version of online/offline aggregate signature is proposed to achieve batch LBS bundle verification based on arbitrary one-way function holding the property of multiplicative homomorphism. Finally, an efficient zero-knowledge range proof based on lightweight one-way hash chain is designed to decide the redundancy of LBS bundles without disclosing vehicular usersโ location privacy. Formal security proof and extensive simulation results demonstrate that our proposed SAVE achieves identity privacy, two levels of location privacy and the practicability in reality.
We propose a definition of ballot secrecy as an indistinguishability game in the computational model of cryptography. Our definition improves upon earlier definitions to ensure ballot secrecy is preserved in the presence of an adversary that controls ballot collection. We also propose a definition of ballot independence as an adaptation of an indistinguishability game for asymmetric encryption. We prove relations between our definitions. In particular, we prove ballot independence is sufficient for ballot secrecy in voting systems with zero-knowledge tallying proofs. Moreover, we prove that building systems from non-malleable asymmetric encryption schemes suffices for ballot secrecy, thereby eliminating the expense of ballot-secrecy proofs for a class of encryption-based voting systems. We demonstrate applicability of our results by analysing the Helios voting system and its mixnet variant. Our analysis reveals that Helios does not satisfy ballot secrecy in the presence of an adversary that controls ballot collection. The vulnerability cannot be detected by earlier definitions of ballot secrecy, because they do not consider such adversaries. We adopt non-malleable ballots as a fix and prove that the fixed system satisfies ballot secrecy.
Houshyar Honar Pajooh, Mohammad A. Rashid, Fakhrul Alam, Serge Demidenko
Abstract The diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2โ3% of the CPU consumption at the peer process and approximately 10โ20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS).
Due to the distributed characteristics of federated learning (FL), the vulnerability of the global model and the coordination of devices are the main obstacle. As a promising solution of decentralization, scalability, and security, leveraging the blockchain in FL has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain-like proof of work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this article introduces a framework for empowering FL using direct acyclic graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in detail, and then, two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of the DAG-FL consensus mechanism. After that, a Poisson process model is formulated to discuss that how to set deployment parameters to maintain DAG-FL stably in different FL tasks. The extensive simulations and experiments show that DAG-FL can achieve better performance in terms of training efficiency and model accuracy compared with the typical existing on-device FL systems as the benchmarks.
The emerging smart city is driving massive transformations of modern cities, facing the huge influx of sensor data from IoT devices. Edge computing distributes computing tasks to the near-edge end, which greatly enhances the service quality of IoT applications, that is, ultralow latency, large capacity, and high throughput. However, due to the constrained resource of IoT devices, currently, systems with a centralized model are vulnerable to attacks, such as DDoS from IoT botnet and central database failure, which can hardly provide high-confidence services. Recently, blockchain with a high security promise is considered to provide new approaches to enhancing the security of IoT systems. However, blockchain and IoT have obvious incompatibility, and low-capacity IoT devices can hardly be incorporated into blockchain with high computing requirements. In this paper, a blockchain-edge computing hybrid system (BEHS) is presented to make the adaptation of blockchain to edge computing and provide trustworthy IoT management services for a smart city. A novel extensible consensus protocol designed for proof-of-work, named proof-of-contribution (PoC), is proposed to regulate the data upload behaviors of nodes, especially the data upload frequency of IoT device nodes, so as to protect the system from attack about frequency. In order to secure the data privacy and authenticity, a data access control scheme is designed by integrating symmetric encryption with asymmetric encryption algorithm. We implemented a concrete BEHS on Ethereum, realized the function of PoC mechanism via smart contracts, and conducted a case study for smart city. The extensive evaluations and analyses show that the proposed PoC mechanism can effectively detect and automatically manage the behavior of nodes, and the time cost of data access control scheme is within an acceptable range.
With rapid urbanization and the advancement of cities and towns, the graph of crime rates is increasing gradually. Blockchain can replace those piled up criminal records with a network where documents are easily accessible and could not be tampered with, making them safe and Secure. Blockchain is a P2P (peer-to-peer network) that helps in the decentralization of data. This system will be based upon the immutability characteristic of blockchain to ensure the integrity and security of data. This blockchain-based process can reduce corruption risk factors by making it easier for third parties to monitor tamper-evident transactions and enabling greater objectivity and consistency, thus enhancing criminal record transparency and accountability. Furthermore, timely access of authentic criminal records to respective administrative authorities will make law enforcement effective.