期刊名称:International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN:2320-9798
电子版ISSN:2320-9801
出版年度:2017
卷号:5
期号:7
页码:13252
DOI:10.15680/IJIRCCE.2017.0507052
出版社:S&S Publications
摘要:Electronic Medical Records (EMR) has been introduced in recent years. Medical Institutions andhealthcare providers are required to store electronic records in a database and provide access for doctors andresearchers. EMR provide health information on as a needed basis for diagnosis and treatment. EMR provideconvenience, but such a system also introduces the new challenges of storing personal information securely. Based onpersonal information a specific person can be identified or quasi-identified. For preventing disclosure of person specificinformation, usually quasi-identified or anonymized data are published. k-Anonymity framework with generalizationand suppression anonymizes the values of the quasi-identifiers which are provided in the EMR. But still k-Anonymityframework has some drawbacks which include re-identification attack. To address this issue, a framework combining k-Anonymity and l-diversity has been proposed. Each equivalence class of anonymized data contains at least l wellrepresented values in the sensitive attributes. While using this approach re-identification attack can be reduced andinformation loss is also minimized.