On-Demand Data Integration on the Cloud

Ghafour, Samer Abdul; Barhamgi, Mahmoud; Ghodous, Parisa

doi:10.1109/cloud.2014.127

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…We implemented the system in Java and evaluated its performance based on a set of 40 medical data sharing services that we built in our previous work [3]. These services access synthetic medical information of more than 30.000 patients.…”

Section: Implementation and Preliminary Evaluationmentioning

confidence: 99%

Privacy-Aware Cloud Services Composition

Ghafour

Ghodous

Bonnet

2015

2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises

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We propose a service-oriented privacy-preserving model for data integration across autonomous clouds. Our model allows to execute aggregations of cloud data sharing services without revealing any extra information to any of the involved services, i.e., none of involved services nor their providers are able to learn/infer any information about the data the other services provide beyond what these services already know.

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Section: Implementation and Preliminary Evaluationmentioning

confidence: 99%

Privacy-Aware Cloud Services Composition

Ghafour

Ghodous

Bonnet

2015

2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises

Self Cite

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Privacy Preserving Data Integration across Autonomous Cloud Services

Ghafour

Ghodous

Bonnet

2015

2015 IEEE 8th International Conference on Cloud Computing

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In this paper, we tackle privacy issues of data sharing services in a cloud environment. We propose a serviceoriented privacy-preserving model for data integration across autonomous clouds. Our model allows to execute aggregations of cloud data sharing services without revealing any extra information to any of the involved services. Thus, involved services enforce locally their privacy policies by applying their own access control models and data anonymization algorithms. We illustrate lack of data protection in current data sources through examples of querying data in a healthcare system.

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Quality Based Data Integration for Enriching User Data Sources in Service Lakes

Alili

Belhajjame

Drira

et al. 2018

2018 IEEE International Conference on Web Services (ICWS)

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In the Big Data era, companies are moving away from traditional data-warehouse solutions whereby expensive and time-consuming ETL (Extract, Transform, Load) processes are used, towards data lakes in order to manage their increasingly growing data. Yet the stored knowledge in companies' databases, even though in the constructed data lakes, can never be complete and up-to-date, because of the continuous production of data. Local data sources often need to be augmented and enriched with information coming from external data sources. Unfortunately, the data enrichment process is one of the manual labors undertaken by experts who enrich data by adding information based on their expertise or select relevant data sources to complete missing information. Such work can be tedious, expensive and time-consuming, making it very promising for automation.

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On-Demand Data Integration on the Cloud

Cited by 3 publications

References 10 publications

Privacy-Aware Cloud Services Composition

Privacy-Aware Cloud Services Composition

Privacy Preserving Data Integration across Autonomous Cloud Services

Quality Based Data Integration for Enriching User Data Sources in Service Lakes

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