EU PROVENANCE Project: An Open Provenance Architecture for Distributed Applications

Vázquez-Salceda, Javier; Álvarez, Sergio; Kifor, Tamás; Varga, László Zsolt; Miles, Simon; Moreau, Luc; Willmott, Steven

doi:10.1007/978-3-7643-8547-7_4

Cited by 4 publications

(3 citation statements)

References 7 publications

(3 reference statements)

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“…While some architectural propositions and even full implementations have incorporated agents as a central component of privacyaware systems [20,27,10,13], these generally lack the capability of having defined clear interactions in a decentralized manner, even across heterogeneous systems. Some other aspects have also been explored (i.e., the verification of privacy policies [14], or the establishment of transparent tracking of provenance [11,25]). Many of these efforts are complementary to our approach, which could benefit from trust and explainability mechanisms [6,7,12], which have shown to be relevant to satisfy high standards in privacy regulations.…”

Section: Related Workmentioning

confidence: 99%

Personal Data Privacy Semantics in Multi-Agent Systems Interactions

Calvaresi

Schumacher

Calbimonte

2020

Lecture Notes in Computer Science

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In recent years, we have witnessed the growth of applications relying on the use and processing of personal data, especially in the health and well-being domains. Users themselves produce these data (e.g., through self-reported data acquisition, or personal devices such as smartphones, smartwatches or other wearables). A key challenge in this context is to guarantee the protection of personal data privacy, respecting the rights of users for deciding about data reuse, consent to data processing and storage, anonymity conditions, or the right to withhold or delete personal data. With the enforcement of recent regulations in this domain, such as the GDPR, applications are required to guarantee compliance, challenging current practices for personal data management. In this paper, we address this problem in the context of decentralized personal data applications, which may need to interact and negotiate conditions of data processing and reuse. Following a distributed paradigm without a top-down organization, we propose an agent-based model in which personal data providers and data consumers are embedded into privacyaware agents capable of negotiating and coordinating data reuse, consent, and policies, using semantic vocabularies for privacy and provenance.

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Section: Related Workmentioning

confidence: 99%

Personal Data Privacy Semantics in Multi-Agent Systems Interactions

Calvaresi

Schumacher

Calbimonte

2020

Lecture Notes in Computer Science

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“…• Data can be shared widely and anonymously in the cloud, provenance is required to verify the authenticity or identity of data [17]. • Some scenarios in cloud computing have clear requirements for provenance of data, such as eScience [18] and healthcare [19], where assurance in the quality of repeatability of results is essential. • Clouds have their own application for provenance, i.e., detection of the origins of faults and security violations [20].…”

Section: Introductionmentioning

confidence: 99%

How to Track Your Data: The Case for Cloud Computing Provenance

Zhang

Kirchberg

et al. 2011

2011 IEEE Third International Conference on Cloud Computing Technology and Science

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Sung Lee HP Laboratories HPL-2012-11 cloud computing; data provenance; accountability; transparency;Provenance, a meta-data describing the derivation history of data, is crucial for the uptake of cloud computing to enhance reliability, credibility, accountability, transparency, and confidentiality of digital objects in a cloud. In this paper, we survey current mechanisms that support provenance for cloud computing, we classify provenance according to its granularities encapsulating the various sets of provenance data for different use cases, and we summarize the challenges and requirements for collecting provenance in a cloud, based on which we show the gap between current approaches to requirements. Additionally, we propose our approach, DataPROVE, that aims to effectively and efficiently satisfy those challenges and requirements in cloud provenance, and to provide a provenance supplemented cloud for better integrity and safety of customers' data.

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“…The general performance of the search (an important requirement in this domain) is also improved. Recently, the EU funded project PROVENANCE developed a distributed organ transplant management system [Vázquez-Salceda et al 2008]. It allows to coordinate tasks among different healthcare institutions, and also traces all performed actions in order to identify problems or audit all the steps followed in the care process.…”

Section: Planning and Resource Allocationmentioning

confidence: 99%

Agent-based management of clinical guidelines

Isern Alarcón

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Les guies de pràctica clínica (GPC) contenen un conjunt d'accions i dades que ajuden a un metge a prendre decisions sobre el diagnòstic, tractament o qualsevol altre procediment a un pacient i sobre una determinada malaltia. És conegut que l'adopció d'aquestes guies en la vida diària pot millorar l'assistència mèdica als pacients, pel fet que s'estandarditzen les pràctiques. Sistemes computeritzats que utilitzen GPC poden constituir part de sistemes d'ajut a la presa de decisions més complexos amb la finalitat de proporcionar el coneixement adequat a la persona adequada, en un format correcte i en el moment precís. L'automatització de l'execució de les GPC és el primer pas per la seva implantació en els centres mèdics. Per aconseguir aquesta implantació final, hi ha diferents passos que cal solucionar com per exemple, l'adquisició i representació de les GPC, la seva verificació formal, i finalment la seva execució. Aquesta Tesi està dirigida en l'execució de GPC i proposa la implementació d'un sistema multi-agent. En aquest sistema els diferents actors dels centres mèdics coordinen les seves activitats seguint un pla global determinat per una GPC. Un dels principals problemes de qualsevol sistema que treballa en l'àmbit mèdic és el tractament del coneixement. En aquest cas s'han hagut de tractar termes mèdics i organitzatius, que s'ha resolt amb la implementació de diferents ontologies. La separació de la representació del coneixement del seu ús és intencionada i permet que el sistema d'execució de GPC sigui fàcilment adaptable a les circumstàncies concretes dels centres, on varien el personal i els recursos disponibles. En paral·lel a l'execució de GPC, el sistema proposat manega preferències del pacient per tal d'implementar serveis adaptats al pacient. En aquesta àrea concretament, a) s'han definit un conjunt de criteris, b) aquesta informació forma part del perfil de l'usuari i serveix per ordenar les propostes que el sistema li proposa, i c) un algoritme no supervisat d'aprenentatge permet adaptar les preferències del pacient segons triï. Finalment, algunes idees d'aquesta Tesi actualment s'estan aplicant en dos projectes de recerca. Per una banda, l'execució distribuïda de GPC, i per altra banda, la representació del coneixement mèdic i organitzatiu utilitzant ontologies. Clinical guidelines (CGs) contain a set of directions or principles to assist the health care practitioner with patient care decisions about appropriate diagnostic, therapeutic, or other clinical procedures for specific clinical circumstances. It is widely accepted that the adoption of guideline-execution engines in daily practice would improve the patient care, by standardising the care procedures. Guideline-based systems can constitute part of a knowledge-based decision support system in order to deliver the right knowledge to the right people in the right form at the right time. The automation of the guideline execution process is a basic step towards its widespread use in medical centres. To achieve this general goal, different topics should be tackled, such as the acquisition of clinical guidelines, its formal verification, and finally its execution. This dissertation focuses on the execution of CGs and proposes the implementation of an agent-based platform in which the actors involved in health care coordinate their activities to perform the complex task of guideline enactment. The management of medical and organizational knowledge, and the formal representation of the CGs, are two knowledge-related topics addressed in this dissertation and tackled through the design of several application ontologies. The separation of the knowledge from its use is fully intentioned, and allows the CG execution engine to be easily customisable to different medical centres with varying personnel and resources. In parallel with the execution of CGs, the system handles citizen's preferences and uses them to implement patient-centred services. With respect this issue, the following tasks have been developed: a) definition of the user's criteria, b) use of the patient's profile to rank the alternatives presented to him, c) implementation of an unsupervised learning method to adapt dynamically and automatically the user's profile. Finally, several ideas of this dissertation are being directly applied in two ongoing funded research projects, including the agent-based execution of CGs and the ontological management of medical and organizational knowledge.

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EU PROVENANCE Project: An Open Provenance Architecture for Distributed Applications

Cited by 4 publications

References 7 publications

Personal Data Privacy Semantics in Multi-Agent Systems Interactions

Personal Data Privacy Semantics in Multi-Agent Systems Interactions

How to Track Your Data: The Case for Cloud Computing Provenance

Agent-based management of clinical guidelines

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