Identity Management, Access Control and Privacy in Integrated Care Platforms: The PICASO Project

Povilionis, Armanas; Arcieri, Franco; Talamo, Maurizio; Ananth, Indiraiith Viii; Schunck, Christian H.; Rosengren, Peter; Thestrup, Jesper; Chiaravalottik, Agostino; Schillaci, Orazio; Gappa, Henrike; Velasco, Carlos A.

doi:10.1109/ccst.2018.8585716

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…In the PICASO project [31], a framework was proposed to enable cross-organization sharing of electronic health records using a cloud-based solution. This project aims El-Kassabi et al Journal of Cloud Computing (2023) 12:10 to provide the required security and privacy measures in addition to service orchestration and data capture and management.…”

Section: Related Workmentioning

confidence: 99%

Deep learning approach to security enforcement in cloud workflow orchestration

Kassabi¹,

Serhani²,

Masud³

et al. 2023

J Cloud Comp

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Supporting security and data privacy in cloud workflows has attracted significant research attention. For example, private patients’ data managed by a workflow deployed on the cloud need to be protected, and communication of such data across multiple stakeholders should also be secured. In general, security threats in cloud environments have been studied extensively. Such threats include data breaches, data loss, denial of service, service rejection, and malicious insiders generated from issues such as multi-tenancy, loss of control over data and trust. Supporting the security of a cloud workflow deployed and executed over a dynamic environment, across different platforms, involving different stakeholders, and dynamic data is a difficult task and is the sole responsibility of cloud providers. Therefore, in this paper, we propose an architecture and a formal model for security enforcement in cloud workflow orchestration. The proposed architecture emphasizes monitoring cloud resources, workflow tasks, and the data to detect and predict anomalies in cloud workflow orchestration using a multi-modal approach that combines deep learning, one class classification, and clustering. It also features an adaptation scheme to cope with anomalies and mitigate their effect on the workflow cloud performance. Our prediction model captures unsupervised static and dynamic features as well as reduces the data dimensionality, which leads to better characterization of various cloud workflow tasks, and thus provides better prediction of potential attacks. We conduct a set of experiments to evaluate the proposed anomaly detection, prediction, and adaptation schemes using a real COVID-19 dataset of patient health records. The results of the training and prediction experiments show high anomaly prediction accuracy in terms of precision, recall, and F1 scores. Other experimental results maintained a high execution performance of the cloud workflow after applying adaptation strategy to respond to some detected anomalies. The experiments demonstrate how the proposed architecture prevents unnecessary wastage of resources due to anomaly detection and prediction.

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Section: Related Workmentioning

confidence: 99%

Deep learning approach to security enforcement in cloud workflow orchestration

Kassabi¹,

Serhani²,

Masud³

et al. 2023

J Cloud Comp

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“…A Distributed Validation Authority (DIVA) ensures that data is only shared if all policies and transaction-specific privacy and security requirements are met. DIVA combines an Access Manager, Identity Manager, and Policy Manager [10,11], which ensure the following: The complete system was tested at the two pilot sites, with a positive feedback from patients and clinicians, especially from the perspective of improvement in the management of the clinical processes and patient empowerment. The system also demonstrated the integration of different clinical data spaces into a homogeneous environment.…”

Section: Scenario 2: Integrated Carementioning

confidence: 99%

Medical Data Spaces in Healthcare Data Ecosystems

Berlage

Claussen

Geisler

et al. 2022

Designing Data Spaces

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Exchange of sensitive medical data between healthcare providers and researchers requires a particularly high level of trust and security. Involving patients and citizen into this process increases transparency and may improve the outcome of preventive, diagnostic, and therapeutic measures. We propose to build the structure of a healthcare ecosystem on the basis of “apps” that not only hold (and exchange) health data but support user interaction and healthcare process management. Emphasis on process support may also be used to improve data quality, which is an important prerequisite for evidence-based medicine and the training and usage of future AI tools.

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Contributing to Current Challenges in Identity and Access Management with Visual Analytics

Puchta¹,

Böhm

Pernul

2019

Data and Applications Security and Privacy XXXIII

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Enterprises have embraced identity and access management (IAM) systems as central point to manage digital identities and to grant or remove access to information. However, as IAM systems continue to grow, technical and organizational challenges arise. Domain experts have an incomparable amount of knowledge about an organization's specific settings and issues. Thus, especially for organizational IAM challenges to be solved, leveraging the knowledge of internal and external experts is a promising path. Applying Visual Analytics (VA) as an interactive tool set to utilize the expert knowledge can help to solve upcoming challenges. Within this work, the central IAM challenges with need for expert integration are identified by conducting a literature review of academic publications and analyzing the practitioners' point of view. Based on this, we propose an architecture for combining IAM and VA. A prototypical implementation of this architecture showcases the increased understanding and ways of solving the identified IAM challenges.

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Identity Management, Access Control and Privacy in Integrated Care Platforms: The PICASO Project

Cited by 4 publications

References 2 publications

Deep learning approach to security enforcement in cloud workflow orchestration

Deep learning approach to security enforcement in cloud workflow orchestration

Medical Data Spaces in Healthcare Data Ecosystems

Contributing to Current Challenges in Identity and Access Management with Visual Analytics

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