ABDM: Anonymity-Based Big Data Management for Protecting Healthcare Data from Privacy Breach
Jin‐Yong Kim, Jaehoon Jeong, Jeonghyeon Kim, Joomin Kim · IEEE Network · 2024
As the healthcare industry has continued to develop and the medical data becomes digitized, the risk of data leakage from attacks grows. All medical data is encrypted and stored to prevent data leakage, but from big data perspective, it is inefficient because the encryption of this medical data lowers the big data processing performance. To address this problem, this paper proposes an Anonymity-based Big Data Management (ABDM) for protecting healthcare data from privacy breach without compromising on performance. The idea of ABDM is to separate and store identity data and healthcare data in databases on different cloud servers. By storing the identity data and healthcare data separately in databases on different cloud servers, hackers are unable to identify whose healthcare data is obtained until they obtain both the identity data and healthcare data. Through experiments in a public cloud, ABDM outperforms existing healthcare data management systems by two times speed.