548 research outputs found
Fine-Grained Access Control with User Revocation in Smart Manufacturing
This research has been founded by the European Union’s Horizon 2020 Research and
Innovation program under grant agreement No. 871518, a project named COLLABS [19].Collaborative manufacturing is a key enabler of Industry 4.0 that requires secure data sharing among multiple parties. However, intercompany data-sharing raises important privacy and security concerns, particularly given intellectual property and business-sensitive information collected by many devices. In this paper, we propose a solution that combines four technologies to address these challenges: Attribute-Based Encryption for data access control, blockchain for data integrity and non-repudiation, Hardware Security Modules for authenticity, and the Interplanetary File System for data scalability. We also use OpenID for dynamic client identification and propose a new method for user revocation in Attribute-Based Encryption. Our evaluation shows that the solution can scale up to 2,000,000 clients while maintaining all security guarantees.European Union’s Horizon 2020, 87151
Blockchain in Oil and Gas Supply Chain: A Literature Review from User Security and Privacy Perspective
Blockchain's influence extends beyond finance, impacting diverse sectors such
as real estate, oil and gas, and education. This extensive reach stems from
blockchain's intrinsic ability to reliably manage digital transactions and
supply chains. Within the oil and gas sector, the merger of blockchain with
supply chain management and data handling is a notable trend. The supply chain
encompasses several operations: extraction, transportation, trading, and
distribution of resources. Unfortunately, the current supply chain structure
misses critical features such as transparency, traceability, flexible trading,
and secure data storage - all of which blockchain can provide. Nevertheless, it
is essential to investigate blockchain's security and privacy in the oil and
gas industry. Such scrutiny enables the smooth, secure, and usable execution of
transactions. For this purpose, we reviewed 124 peer-reviewed academic
publications, conducting an in-depth analysis of 21 among them. We classified
the articles by their relevance to various phases of the supply chain flow:
upstream, midstream, downstream, and data management. Despite blockchain's
potential to address existing security and privacy voids in the supply chain,
there is a significant lack of practical implementation of blockchain
integration in oil and gas operations. This deficiency substantially challenges
the transition from conventional methods to a blockchain-centric approach
Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordCritical infrastructure systems are vital to underpin
the functioning of a society and economy. Due to ever-increasing
number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected,
security and scalability are becoming burning concerns for
critical infrastructures in industry 4.0. The blockchain technology
is essentially a distributed and secure ledger that records all
the transactions into a hierarchically expanding chain of blocks.
Edge computing brings the cloud capabilities closer to the
computation tasks. The convergence of blockchain and edge
computing paradigms can overcome the existing security and
scalability issues. In this paper, we first introduce the IoT/IIoT
critical infrastructure in industry 4.0, and then we briefly present
the blockchain and edge computing paradigms. After that, we
show how the convergence of these two paradigms can enable
secure and scalable critical infrastructures. Then, we provide a
survey on state-of-the-art for security and privacy, and scalability
of IoT/IIoT critical infrastructures. A list of potential research
challenges and open issues in this area is also provided, which
can be used as useful resources to guide future research.Engineering and Physical Sciences Research Council (EPSRC
Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning
Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment
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