A clustering‐based anonymization approach for privacy‐preserving in the healthcare cloud. (13th July 2021)

• Record Type: Journal Article
• Authors: Abbasi, Afsoon; Mohammadi, Behnaz
• Is Part Of: Concurrency and computation. Volume 34:Number 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-13
• Subjects: healthcare cloud -- information loss -- K‐anonymity -- K‐means++ -- normal distribution function -- privacy‐preserving; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6487
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Today, modern healthcare systems rely on advanced computational technologies, including cloud‐based systems, to gather and examine personal health data on a large scale. The use of advanced cloud services technologies, such as software as a service, application as a service, is challenging for end‐users of cloud systems to protect sensitive data in their health applications. According to the importance of publishing data in the cloud, the information should be recorded and handled somehow so that any individuals' identity remains hidden. Therefore, one of the critical privacy challenges is protecting the quality of published data and privacy‐preserving on the healthcare cloud simultaneously. The K‐anonymity technology is one of the prevalent methods used for privacy‐preserving. In this article, we suggest a novel approach based on the clustering process using the K‐means++ method to achieve an optimal k‐anonymity algorithm. Also, we use the normal distribution function to delete data that is less frequent, which can be improved the quality of anonymized data. Extensive experiments show the proposed method has been able to reduce information loss 1.5 times and execution time 3.5 times compared to AKA and GCCG algorithms. Also, it is highly scalable than others.