Abstract:
Due to the increase in the emergence of health care system information, patient records are needed for analyzing certain diseases. It leads to the need for privacy and several challenges in the current health care system. Privacy-preserving data publishing is essential to protect the patient's record from numerous attacks. The main aim of privacy-preserving data publishing is to safeguard an individuals' personal information, though it is made available for various analysis purposes. Initially, the paper has analyzed different services of an electronic health record, need for privacy and has proposed a novel anonymization technique for electronic health records. The proposed approach will overcome the following drawbacks i) generalization of all attributes which significantly reduces the information ii) identity disclosure even with adversaries having intense background knowledge and iii) the trade-off between privacy and utility. Compared to the existing system the proposed approach achieves a better result by using hierarchical id-based generalization approach. Hence, the proposed approach helps significantly in protecting individuals' information even though an intruder has intense background knowledge. Additionally, it focuses on achieving balanced utility in the anonymized data by avoiding over generalization. The proposed approach consists of four phases i) vertical partitioning, ii) Quasi-identifier bucket(QB) anonymization, iii) Sensitive attribute bucket(SAB) anonymization iv) Evaluation of classification accuracy. As per the experimental result, the proposed approach achieves improved data privacy and utility in privacy-preserving data publishing. The experimental results are not evaluated using the utility metrics. However, the results, evidently illustrate that the proposed algorithm achieves improved accuracy than the standard utility aware data anonymization algorithm in health care records. The proposed approach thwarts the identity disclosure and attribute disclosure for the static data.
Page(s):
5526-5541
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 17, Year: 2022
Keywords:
Anonymization
,
privacy
,
Generalization
,
Utility
,
health care
,
Classification Model