Abstract
With the advances of data mining and the pervasiveness of cloud computing, online medical diagnosis service has been extensively applied in e-heathcare field, and brought great conveniences to people's life. However, due to the insufficient data sharing among healthcare centers under the security and privacy concerns of medical information, the flourish of online medical diagnosis service still faces many severe challenges including diagnostic accuracy issues. In this paper, in order to address the security issues and improve the accuracy of online medical diagnosis service, we propose a new privacy-preserving collaborative model learning scheme with skyline computation, called PCML. With PCML, healthcare centers can securely learn a global diagnosis model with their local diagnosis models in the assistance of cloud, and the sensitive medical data of each healthcare center is well protected. Specifically, with a secure multi-party vector comparison algorithm (SMVC), all local diagnosis models are encrypted by their owners before being sent to the cloud, and can be directly operated without decryption. Detailed security analysis shows that PCML can resist security threats in the semi-honest model. Moreover, PCML is implemented with medical datasets from UCI machine learning repository, and extensive simulation results demonstrate that PCML is efficient and can be implemented effectively.
Highlights
In recent years, the online medical diagnosis system [1], which can provide medical diagnosis service anywhere and anytime, has attracted considerable interest
With collected medical data, healthcare centers can generate diagnosis models via medical data mining with skyline query, which assists them in offering online medical diagnosis services, and allows users to check their health conditions expediently
To achieve the quality of online medical diagnosis service, we construct a secure multi-party vector comparison (SMVC) algorithm based on paillier cryptosystem with secret sharing, which supports lossless collaborative model learning while protecting healthcare centers’ privacy
Summary
The online medical diagnosis system [1], which can provide medical diagnosis service anywhere and anytime, has attracted considerable interest. Healthcare centers expect to learn a more accurate global diagnosis model collaboratively with their local medical information (i.e., local skyline diagnosis models) for offering better services. It is of great importance to develop a privacy-preserving collaborative model learning scheme over multiple healthcare centers for online medical diagnosis system. We propose a privacy-preserving collaborative model learning scheme with skyline computation, named PCML. PCML addresses the privacy and data security issues of collaborative model learning for skyline computation. To achieve the quality of online medical diagnosis service, we construct a secure multi-party vector comparison (SMVC) algorithm based on paillier cryptosystem with secret sharing, which supports lossless collaborative model learning while protecting healthcare centers’ privacy.
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