ECMO: An Efficient and Confidential Outsourcing Protocol for Medical Data
Year: 2024
Type: article
Abstract: Cloud computing has significantly advanced medical data storage capabilities, enabling healthcare institutions to outsource data management. However, this shift introduces critical security and privacy risks, as sensitive patient information is stored on untrusted third-party servers. Existing cryptographic solutions, such as searchable encryption, offer some security guarantees but struggle with challenges like leakage-based attacks, high computational overhead, and limited scalability. To address these limitations in medical data outsourcing, we present ECMO, a novel protocol that combines an ordered additive secret sharing algorithm with a unique index permutation method. This approach efficiently outsources medical data while safeguarding both the data itself and access patterns from potential leakage. Our experimental results demonstrate ECMO's efficiency and scalability, with a single store operation containing 500 keywords taking only$42.5 \;\mu s$on average.
Authors: Xiangyi Meng, Yuefeng Du, Cong Wang
Institution: City University of Hong Kong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,995
OpenAlex ID: W4404688149