Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case

Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit

Reinforcing Digital Trust for Cloud Manufacturing Through Data Provenance Using Ethereum Smart Contracts

Cloud Manufacturing(CMfg) is an advanced manufacturing model that caters to fast-paced agile requirements (Putnik, 2012). For manufacturing complex products that require extensive resources, manufacturers explore advanced manufacturing techniques like CMfg as it becomes infeasible to achieve high standards through complete ownership of manufacturing artifacts (Kuan et al., 2011). CMfg, with other names such as Manufacturing as a Service (MaaS) and Cyber Manufacturing (NSF, 2020), addresses the shortcoming of traditional manufacturing by building a virtual cyber enterprise of geographically distributed entities that manufacture custom products through collaboration. With manufacturing venturing into cyberspace, Digital Trust issues concerning product quality, data, and intellectual property security, become significant concerns (R. Li et al., 2019). This study establishes a trust mechanism through data provenance for ensuring digital trust between various stakeholders involved in CMfg. A trust model with smart contracts built on the Ethereum blockchain implements data provenance in CMfg. The study covers three data provenance models using Ethereum smart contracts for establishing digital trust in CMfg. These are Product Provenance, Order Provenance, and Operational Provenance. The models of provenance together address the most important questions regarding CMfg: What goes into the product, who manufactures the product, who transports the products, under what conditions the products are manufactured, and whether regulatory constraints/requisites are met

Provenance and Trust

The interest in data provenance and trust has been increasing in the last years and the community is putting now a lot of effort in finding a standard model representation. The W3C provenance incubator group is focused on this area, analyzing different provenance models and making mappings between them and the Open Provenance Model (OPM)[1], which is the model they intend to make the standard. We want to develop a provenance system based in OPM and a trust algorithm from that provenance information. Our aim will be a platform that will not store the contents generated by the users, but it will store all the references to them, the opinions from the users, information from social networks, etc. to obtain semantic information from the Web. In this context, being able to predict the trust of a source or being able to track the content we’ve generated is a great choice for any use

Provenance-based trust for grid computing: Position Paper

Current evolutions of Internet technology such as Web Services, ebXML, peer-to-peer and Grid computing all point to the development of large-scale open networks of diverse computing systems interacting with one another to perform tasks. Grid systems (and Web Services) are exemplary in this respect and are perhaps some of the first large-scale open computing systems to see widespread use - making them an important testing ground for problems in trust management which are likely to arise. From this perspective, today's grid architectures suffer from limitations, such as lack of a mechanism to trace results and lack of infrastructure to build up trust networks. These are important concerns in open grids, in which "community resources" are owned and managed by multiple stakeholders, and are dynamically organised in virtual organisations. Provenance enables users to trace how a particular result has been arrived at by identifying the individual services and the aggregation of services that produced such a particular output. Against this background, we present a research agenda to design, conceive and implement an industrial-strength open provenance architecture for grid systems. We motivate its use with three complex grid applications, namely aerospace engineering, organ transplant management and bioinformatics. Industrial-strength provenance support includes a scalable and secure architecture, an open proposal for standardising the protocols and data structures, a set of tools for configuring and using the provenance architecture, an open source reference implementation, and a deployment and validation in industrial context. The provision of such facilities will enrich grid capabilities by including new functionalities required for solving complex problems such as provenance data to provide complete audit trails of process execution and third-party analysis and auditing. As a result, we anticipate that a larger uptake of grid technology is likely to occur, since unprecedented possibilities will be offered to users and will give them a competitive edge

A Survey of Provenance Leveraged Trust in Wireless Sensor Networks

A wireless sensor network is a collection of self-organized sensor nodes. WSNs have many challenges such as lack of a centralized network administration, absence of infrastructure, low data transmission capacity, low bandwidth, mobility, lack of connectivity, limited power supply and dynamic network topology. Due to this vulnerable nature, WSNs need a trust architecture to keep the quality of the network data high for a longer time. In this work, we aim to survey the proposed trust architectures for WSNs. Provenance can play a key role in assessing trust in these architectures. However not many research have leveraged provenance for trust in WSNs. We also aim to point out this gap in the field and encourage researchers to invest in this topic. To our knowledge our work is unique and provenance leveraged trust work in WSNs has not been surveyed before. Keywords:Provenance, Trust, Wireless Sensor Networks  

A Formal Account of the Open Provenance Model

On the Web, where resources such as documents and data are published, shared, transformed, and republished, provenance is a crucial piece of metadata that would allow users to place their trust in the resources they access. The Open Provenance Model (OPM) is a community data model for provenance that is designed to facilitate the meaningful interchange of provenance information between systems. Underpinning OPM is a notion of directed graph, where nodes represent data products and processes involved in past computations, and edges represent dependencies between them; it is complemented by graphical inference rules allowing new dependencies to be derived. Until now, however, the OPM model was a purely syntactical endeavor. The present paper extends OPM graphs with an explicit distinction between precise and imprecise edges. Then a formal semantics for the thus enriched OPM graphs is proposed, by viewing OPM graphs as temporal theories on the temporal events represented in the graph. The original OPM inference rules are scrutinized in view of the semantics and found to be sound but incomplete. An extended set of graphical rules is provided and proved to be complete for inference. The paper concludes with applications of the formal semantics to inferencing in OPM graphs, operators on OPM graphs, and a formal notion of refinement among OPM graphs

Trust threads: minimal provenance and data publication and reuse

Presented at the National data integrity conference: enabling research: new challenges & opportunities held on May 7-8, 2015 at Colorado State University, Fort Collins, Colorado. Researchers, administrators and integrity officers are encountering new challenges regarding research data and integrity. This conference aims to provide attendees with both a high level understanding of these challenges and impart practical tools and skills to deal with them. Topics will include data reproducibility, validity, privacy, security, visualization, reuse, access, preservation, rights and management.Beth A. Plale is the Director, Data to Insight Center, Managing Director, Pervasive Technology Institute and a Professor, School of Informatics and Computing Indiana University. Dr. Plale has broad research and governance interest in information, in long-term preservation and access to scientific data, and in enabling computational access to large and complex data for broader use. Her specific research interest are in metadata and data provenance, trusted data repositories and enclaves, data analysis and text mining of big data, and workflow systems. Plale teaches in the Data Science Program at Indiana University Bloomington. She is deeply engaged in interdisciplinary research and education and has substantive experience in developing stable and useable scientific cyberinfrastructure.PowerPoint presentation given on May 8, 2015