An Auditable Reputation Service for Collective Adaptive Systems

Packer, Heather S.; Drăgan, Laura; Moreau, Luc

doi:10.1007/978-3-319-08681-1_8

Cited by 12 publications

(10 citation statements)

References 25 publications

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“…Typically, the models proposed for these systems rely on distributed notions of trust and reputation. More precisely, trust management is distributed over all the nodes, which may collaborate with each others in order to exchange and aggregate personal opinions, calculate trust scores of target nodes, and disseminate such values [24,28,8,21]. For instance, trustworthy sensor networks base their ability to collectively process sensed data on decentralized reputation systems [9,29,11,26,22].…”

Section: Introductionmentioning

confidence: 99%

A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

Aldini

2016

Electron. Proc. Theor. Comput. Sci.

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Trust and reputation models for distributed, collaborative systems have been studied and applied in several domains, in order to stimulate cooperation while preventing selfish and malicious behaviors. Nonetheless, such models have received less attention in the process of specifying and analyzing formally the functionalities of the systems mentioned above. The objective of this paper is to define a process algebraic framework for the modeling of systems that use (i) trust and reputation to govern the interactions among nodes, and (ii) communication models characterized by a high level of adaptiveness and flexibility. Hence, we propose a formalism for verifying, through model checking techniques, the robustness of these systems with respect to the typical attacks conducted against webs of trust

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Section: Introductionmentioning

confidence: 99%

A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

Aldini

2016

Electron. Proc. Theor. Comput. Sci.

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“…Packer et al discussed provenance for CAS and argued that provenance data helps in making CAS more transparent and accountable, and also help in assessment of users' trust. The authors argue that provenance makes systems more accountable as provenance data provides information about the use of data and decision‐making processes.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, Missier and Moreau 23 discussed a concrete scenario of social computing; they also argued that some concepts, such as task delegations and incentives, are still problematic to express using Prov-DM. Packer et al discussed provenance for CAS 40 and argued that provenance data helps in making CAS more transparent and accountable, and also help in assessment of users' trust. The authors argue that provenance makes systems more accountable as provenance data provides information about the use of data and decision-making processes.…”

Section: Related Workmentioning

confidence: 99%

Application of provenance in social computing: A case study

Riveni

Nguyen

Aktaş

et al. 2018

Concurrency and Computation

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Summary Complex systems such as Collective Adaptive Systems that include a variety of resources, are increasingly being designed to include people in task‐execution. Collectives, encapsulating human resources/services, represent one type of an application within which people with different type of skills can be engaged to solve one common problem or work on the same project. Mechanisms of managing social collectives are dependent on functional and non‐functional parameters of members of social collectives. In this work, we investigate the benefits provenance can offer to social computing and trade‐off implications. We show experimental results of how provenance data can help better visualize interaction and performance data during a collective's run‐time. We present novel metrics that can be derived from provenance, and lastly, we discuss privacy implications. If utilized ethically, provenance can help in developing more efficient provisioning and management mechanisms in social computing.

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“…Another work by Garijo also extends the use of PROV-O for documenting workflow plans in terms of steps and variables, known as P-PLAN [18]. Packer et al developed a provenance ontology in social computations domain with the aim to measure a community based-service reputation [19]. This ontology is used to explain entities used in social computation.…”

Section: A Provenancementioning

confidence: 99%

prFood: Ontology Principles for Provenance and Risk in the Food Domain

Batlajery¹,

Weal²,

Chapman³

et al. 2018

2018 IEEE 12th International Conference on Semantic Computing (ICSC)

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Abstract-An ontology, a formal representation of domain knowledge, is an integral building block of the semantic web and is important in building an application within interdisciplinary domains. In the food domain, regulations require Food Business Operators (FBOs) to comply with traceability and track-ability measures when handling food in order to assess risk. In this paper, we identify several requirements to model food, food history, and risk of contamination in order to support the safety regulations of food. Using those requirements, we also identify and apply several design principles for building an ontology called prFood that encompasses interdisciplinary domains, food and risk. In order to apply safe handling of food in the food supply chain, we integrate and incorporate the pre-existing food ontologies with prFood by implementing a mapping procedure. Finally, we validate our approach by answering several use cases derived from EU and/or UK Food Regulations.

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An Auditable Reputation Service for Collective Adaptive Systems

Cited by 12 publications

References 25 publications

A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

Application of provenance in social computing: A case study

prFood: Ontology Principles for Provenance and Risk in the Food Domain

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