Title :
Significance of Term Relationships on Anonymization
Author :
Anandan, Balamurugan ; Clifton, Chris
Author_Institution :
Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN, USA
Abstract :
Sharing data provides great benefit to the research community. But disclosing identifiable, sensitive information such as medical records can cause irreparable damage. A number of methods have been proposed to anon Mize sensitive information. With some approaches, term relationships in the data may help to re-identify the original data given the de-identified data. This papers studies the significance of correlation in data and then analyzes the effect on anonymization techniques including t-plausibility and k-manonymity. Finally, we show how to address correlation in thet-plausibility model.
Keywords :
data mining; data privacy; medical information systems; text analysis; anonymization techniques; data mining; data sharing; k-manonymity; medical records; privacy; t-plausibility model; term relationships; text documents; Cancer; Correlation; Databases; Liver; Lungs; Pain; Semantics; Data mining; Privacy;
Conference_Titel :
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on
Conference_Location :
Lyon
Print_ISBN :
978-1-4577-1373-6
Electronic_ISBN :
978-0-7695-4513-4
DOI :
10.1109/WI-IAT.2011.240
