scholarly journals A Novel Multi-Secret Sharing Approach for Secure Data Warehousing and On-Line Analysis Processing in the Cloud

2019 ◽  
pp. 483-506
Author(s):  
Varunya Attasena ◽  
Nouria Harbi ◽  
Jérôme Darmont

Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud raises legitimate security concerns. In this paper, the authors propose a new multi-secret sharing approach for deploying data warehouses in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. The authors first validate the relevance of their approach theoretically and then experimentally with both a simple random dataset and the Star Schema Benchmark. The authors also demonstrate its superiority to related methods.

2015 ◽  
Vol 11 (2) ◽  
pp. 22-43 ◽  
Author(s):  
Varunya Attasena ◽  
Nouria Harbi ◽  
Jérôme Darmont

Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud raises legitimate security concerns. In this paper, the authors' propose a new multi-secret sharing approach for deploying data warehouses in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. The authors' first validate the relevance of their approach theoretically and then experimentally with both a simple random dataset and the Star Schema Benchmark. The authors also demonstrate its superiority to related methods.


Author(s):  
Jérôme Darmont ◽  
Emerson Olivier

In this context, the warehouse measures, though not necessarily numerical, remain the indicators for analysis, and analysis is still performed following different perspectives represented by dimensions. Large data volumes and their dating are other arguments in favor of this approach (Darmont et al., 2003). Data warehousing can also support various types of analysis, such as statistical reporting, on-line analysis (OLAP) and data mining. The aim of this article is to present an overview of the existing data warehouses for biomedical data and to discuss the issues and future trends in biomedical data warehousing. We illustrate this topic by presenting the design of an innovative, complex data warehouse for personal, anticipative medicine.


2006 ◽  
Vol 22 (04) ◽  
pp. 248-252
Author(s):  
Song Sang ◽  
Hua-jun Li

A new aided decision support system (DSS) based on data warehouses is discussed. It is composed of data warehouses, on-line analysis processing, and data mining, which is new in the field of DSS. The essential principle, the setting up of the model, the development environment, the main system interface, and a sketch of the theory framework of the new DSS architecture are also described. The decision support system was applied to data abstraction for evaluating ship form scenarios. Tests have shown it to be practical and dependable in complex systems, such as in the demonstration of ship forms.


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