Paper Info
Title
Protecting medical data for analyses
Abstract
In the last few decades, medical data has mainly been a by-product of daily operations. In general, not much has been used for analytical purposes, other than reporting and simple statistics. Just recently, it has become clear that data are important assets if used for analyses that help decision-making. To be able to analyse the data, one needs to have full access to the relevant sources. This may contradict one of the paramount requirements - to have secure, private data - especially if the data analyst is outsourced and not directly affiliated with the data owner, as is often the case in medical environments. In this paper, we present data analyses from the data protection point of view. We propose a solution for outsourced model-based data analyses. A formal framework for protecting the data that leaves the organization's boundary, based on the relational data model's abstract data type, is presented. The data and the data structure are modified so that the process of data analysis can still take place and the results can still be obtained, but the data content itself is hard to reveal. Once the data analysis results are returned, the inverse process discloses the meaning of the model to the data owners.
Year
DOI
Venue
2002
10.1109/CBMS.2002.1011362
CBMS
Keywords
Field
DocType
data analyst,relational abstract data type,medical information systems,relational databases,data owner,data assets,outsourced model-based data analysis,statistics,outsourced model-based data analyses,data structure modification,organizational boundary,abstract data type,data protection point,data content,data analysis,data structure,medical data protection,reporting,medical data analysis,data analyses result,data modification,data models,present data analysis,abstract data types,model meaning,medical decision-making,protecting medical data,relational data model,secure private data,data owners,data source access,formal framework,security of data,environmental economics,data structures,machine intelligence,data mining,computer science,data protection,databases,relational data
Data science,Abstract data type,Data warehouse,Data mining,Data structure,Data modeling,Data quality,Computer science,Machine-readable data,Data virtualization,Data independence
Conference
ISSN
ISBN
Citations 
1063-7125
0-7695-1614-9
3
PageRank 
References 
Authors
0.39
4
5
Name
Order
Citations
PageRank
Bostjan Brumen126025.48
Tatjana Welzer2219120.18
Marjan Družovec3309.23
Izidor Golob4243.89
Hannu Jaakkola530260.55