EBMD: Efficient Based Medical Data Share In Database

Abstract

Cloud computing has significantly reshaped the management of medical data by enabling healthcare organizations to outsource storage and processing to third-party service providers. Although this paradigm offers advantages such as scalability, flexibility, and reduced infrastructure costs, it also raises critical concerns regarding data security and patient privacy, especially when sensitive information is stored on untrusted servers. Existing cryptographic approaches, including searchable encryption, attempt to mitigate these risks but often face challenges such as information leakage, computational inefficiency, and limited scalability in large datasets.

To overcome these limitations, this paper introduces EBMD, a novel secure outsourcing framework that combines an ordered additive secret sharing mechanism with an innovative index permutation strategy. The proposed method ensures both data confidentiality and query privacy by effectively hiding data contents as well as access patterns from potential adversaries. Furthermore, EBMD is designed to be computationally efficient and scalable, making it suitable for large-scale healthcare systems.

Experimental results demonstrate that EBMD outperforms existing techniques in terms of efficiency, security, and scalability, while maintaining minimal storage overhead. These findings highlight the potential of the proposed approach for secure and practical deployment in cloud-based medical data management systems.