A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

Dubovitskaya, Alevtina; Urovi, Visara; Vasirani, Matteo; Aberer, Karl; Schumacher, Michael

doi:10.1007/978-3-319-18467-8_39

Cited by 26 publications

(12 citation statements)

References 21 publications

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“…This guarantees k -anonymity property in a distributed environment and therefore ensures the data privacy. The algorithms are described in detail in [17]. Degeneralization module will be implemented to improve the quality of the data with the growth of RSDB by mitigating the data losses due to applying anonymization algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…Every contributor can propose to modify the parameters during the process of agents negotiation described in Section 2.2.2. In [17] we proposed algorithms that allow the release of medical data for the research purposes from different LDBs independently, while preserving the anonymity property of RSDB. Generalization rules are expressed as binary trees and are used to achieve k -anonymity and maximum utility without revealing nonanonymized QID values to the system.…”

Section: Methodsmentioning

confidence: 99%

“…Before the data are transferred the anonymization algorithms [17] are applied. This guarantees that k -anonymity of RSDB is preserved, and, therefore, patient privacy will not be violated.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Multiagent System for Dynamic Data Aggregation in Medical Research

Dubovitskaya

Urovi

Barba

et al. 2016

BioMed Research International

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The collection of medical data for research purposes is a challenging and long-lasting process. In an effort to accelerate and facilitate this process we propose a new framework for dynamic aggregation of medical data from distributed sources. We use agent-based coordination between medical and research institutions. Our system employs principles of peer-to-peer network organization and coordination models to search over already constructed distributed databases and to identify the potential contributors when a new database has to be built. Our framework takes into account both the requirements of a research study and current data availability. This leads to better definition of database characteristics such as schema, content, and privacy parameters. We show that this approach enables a more efficient way to collect data for medical research.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Multiagent System for Dynamic Data Aggregation in Medical Research

Dubovitskaya

Urovi

Barba

et al. 2016

BioMed Research International

Self Cite

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“…We 4 http://www.hl7.org. also developed a privacy-preserving algorithm for distributed data aggregation for medical research [7] that can be used to ensure anonymity of the patients. However, anonymization may a ect the utility of the data.…”

Section: From Drop Of Blood To Research Database and Backmentioning

confidence: 99%

Tucuxi

Dubovitskaya

Buclin²,

Schumacher

et al. 2017

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

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Therapeutic Drug Monitoring (TDM) is a key concept in precision medicine. The goal of TDM is to avoid therapeutic failure or toxic e ects of a drug due to insu cient or excessive circulating concentration exposure related to between-patient variability in the drug's disposition. We present TUCUXI-an intelligent system for TDM. By making use of embedded mathematical models, the software allows to compute maximum likelihood individual predictions of drug concentrations from population pharmacokinetic data, based on patient's parameters and previously observed concentrations. TUCUXI was developed to be used in medical practice, to assist clinicians in taking dosage adjustment decisions for optimizing drug concentration levels. This software is currently being tested in a University Hospital. In this paper we focus on the process of software integration in clinical work ow. The modular architecture of the software allows us to plug in a module enabling data aggregation for research purposes. This is an important feature in order to develop new mathematical models for drugs, and thus to improve TDM. Finally we discuss ethical issues related to the use of an automated decision support system in clinical practice, in particular if it allows data aggregation for research purposes. CCS CONCEPTS • Information systems → Expert systems; • Applied computing → Health care information systems;

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“…proper access control policy languages and standards. Dubovitskaya 2015 [31] proposed an architecture of a secure and scalable privacy preserving eHealth cloud system that allows to store and efficiently search over patient data used for the treatment and an algorithm that allows to build a database with patients' data for the research purposes. With the proposed algorithm we only preserve the utility of the RSDB( Non -Redundant representative sequence).…”

Section: Review Of Some Security Relatedmentioning

confidence: 99%

A Survey on Security Challenges of Healthcare Analysis Over Cloud

Jain¹,

Singh²

2017

IJERT

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The evolution of cloud computing in healthcare has revolutionized how the computing is abstracted and utilized on remote third party infrastructure. Introducing the cloud services in the health sector not only facilitates the exchange of electronic medical records among the hospitals and clinics, but also enables the cloud to act as a medical record storage center. Moreover, shifting to the cloud environment relieves the healthcare organizations of the tedious tasks of infrastructure management and also minimizes development and maintenance costs. Because of probable disclosure of medical records stored and exchanged in the cloud, the patients' privacy concerns should essentially be considered when designing the security and privacy mechanisms. Security strength defines the success of any network service. This survey paper aims to discuss, analyze security challenges and available solutions in cloud computing. Various approaches have been used to preserve the security of the health information in the cloud environment.

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A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

Cited by 26 publications

References 21 publications

A Multiagent System for Dynamic Data Aggregation in Medical Research

A Multiagent System for Dynamic Data Aggregation in Medical Research

Tucuxi

A Survey on Security Challenges of Healthcare Analysis Over Cloud

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