A Provenance-Based Policy Control Framework for Cloud Services

Ali, Mufajjul; Moreau, Luc

doi:10.1007/978-3-319-16462-5_10

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In the security discussion, three papers were presented: [56] is a survey for security in seven provenance systems, [45] proposes a policy control framework for integrating and developing services that generate and use provenance. [63] provides security controls for protecting provenance information.…”

Section: Provenancementioning

confidence: 99%

Provenance in Open Data Entity-Centric Aggregation

Giunchiglia

Reyad

2015

Lecture Notes in Computer Science

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An increasing number of web services these days require combining data from several data providers into an aggregated database. Usually this aggregation is based on the linked data approach. On the other hand, the entity-centric model is a promising data model that outperforms the linked data approach because it solves the lack of explicit semantics and the semantic heterogeneity problems. However, current open data which is available on the web as raw datasets can not be used in the entity-centric model before processing them with an import process to extract the data elements and insert them correctly in the aggregated entity-centric database. It is essential to certify the quality of these imported data elements, especially the background knowledge part which acts as input to semantic computations, because the quality of this part affects directly the quality of the web services which are built on top of it. Furthermore, the aggregation of entities and their attribute values from different sources raises three problems: the need to trace the source of each element, the need to trace the links between entities which can be considered equivalent and the need to handle possible conflicts between different values when they are imported from various data sources. In this thesis, we introduce a new model to certify the quality of a back ground knowledge base which separates linguistic and language independent elements. We also present a pipeline to import entities from open data repositories to add the missing implicit semantics and to eliminate the semantic heterogeneity. Finally, we show how to trace the source of attribute values coming from different data providers; how to choose a strategy for handling possible conflicts between these values; and how to keep the links between identical entities which represent the same real world entity.

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Section: Provenancementioning

confidence: 99%

Provenance in Open Data Entity-Centric Aggregation

Giunchiglia

Reyad

2015

Lecture Notes in Computer Science

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“…Provenance on cloud computing can help in running forensic analysis of past events if recorded separately in addition to the data generated by the events [24]. This can ensure transparency, data fidelity and protection, privacy issues of the data collected, quality control, and intellectual property protection [25]. In cloud manufacturing, the IIoTs and the cyber physical systems enabled by them can be tracked closely using their provenance data [26][27][28].…”

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confidence: 99%

Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0

Umer,

Belay,

Gouveia

2024

Electronics

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Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.

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Effectuation and Future of Provenance in Various Fields

Bhardwaj

Bawa

2021

Innovations in Information and Communication Technologies (IICT-2020)

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A Provenance-Based Policy Control Framework for Cloud Services

Cited by 5 publications

References 16 publications

Provenance in Open Data Entity-Centric Aggregation

Provenance in Open Data Entity-Centric Aggregation

Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0

Effectuation and Future of Provenance in Various Fields

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