project maker
09-08-2014, 02:57 PM
Survey of Privacy Protection for Medical Data
Abstract
Expanded scientific knowledge, combined with the development of the net and widespread use of computers have increased the need for strong privacy protection for medical records. We have all heard stories of harassment that has resulted because of the lack of adequate privacy protection of medical records.
"...medical information is routinely shared with and viewed by third parties who are not involved in patient care .. The American Medical Records Association has identified twelve categories of information seekers outside of the health care industry who have access to health care files, including employers, government agencies, credit bureaus, insurers, educational institutions, and the media."
K-Anonymity
Sweeny came up with a formal protection model named k-anonymity
What is K-Anonymity?
If the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release.
Example.
If you try to identify a man from a release, but the only information you have is his birth date and gender. There are k people meet the requirement. This is k-Anonymity.
Conclusions
m-invariant table support republication of dynamic datasets
Guarding nodes allow individuals to describe their privacy requirements better
Anatomy outperforms generalization by allowing much more accurate data analysis on the published data.
Abstract
Expanded scientific knowledge, combined with the development of the net and widespread use of computers have increased the need for strong privacy protection for medical records. We have all heard stories of harassment that has resulted because of the lack of adequate privacy protection of medical records.
"...medical information is routinely shared with and viewed by third parties who are not involved in patient care .. The American Medical Records Association has identified twelve categories of information seekers outside of the health care industry who have access to health care files, including employers, government agencies, credit bureaus, insurers, educational institutions, and the media."
K-Anonymity
Sweeny came up with a formal protection model named k-anonymity
What is K-Anonymity?
If the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release.
Example.
If you try to identify a man from a release, but the only information you have is his birth date and gender. There are k people meet the requirement. This is k-Anonymity.
Conclusions
m-invariant table support republication of dynamic datasets
Guarding nodes allow individuals to describe their privacy requirements better
Anatomy outperforms generalization by allowing much more accurate data analysis on the published data.