Title |
Some methods for blindfolded record linkage
|
---|---|
Published in |
BMC Medical Informatics and Decision Making, June 2004
|
DOI | 10.1186/1472-6947-4-9 |
Pubmed ID | |
Authors |
Tim Churches, Peter Christen |
Abstract |
The linkage of records which refer to the same entity in separate data collections is a common requirement in public health and biomedical research. Traditionally, record linkage techniques have required that all the identifying data in which links are sought be revealed to at least one party, often a third party. This necessarily invades personal privacy and requires complete trust in the intentions of that party and their ability to maintain security and confidentiality. Dusserre, Quantin, Bouzelat and colleagues have demonstrated that it is possible to use secure one-way hash transformations to carry out follow-up epidemiological studies without any party having to reveal identifying information about any of the subjects - a technique which we refer to as "blindfolded record linkage". A limitation of their method is that only exact comparisons of values are possible, although phonetic encoding of names and other strings can be used to allow for some types of typographical variation and data errors. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 4% |
Brazil | 2 | 3% |
Ireland | 1 | 1% |
Switzerland | 1 | 1% |
Germany | 1 | 1% |
United Kingdom | 1 | 1% |
Sweden | 1 | 1% |
New Zealand | 1 | 1% |
Luxembourg | 1 | 1% |
Other | 0 | 0% |
Unknown | 65 | 84% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 19 | 25% |
Student > Ph. D. Student | 17 | 22% |
Student > Master | 11 | 14% |
Other | 5 | 6% |
Professor > Associate Professor | 4 | 5% |
Other | 13 | 17% |
Unknown | 8 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 29 | 38% |
Medicine and Dentistry | 18 | 23% |
Social Sciences | 5 | 6% |
Business, Management and Accounting | 4 | 5% |
Agricultural and Biological Sciences | 2 | 3% |
Other | 8 | 10% |
Unknown | 11 | 14% |