Abstract
Medical prediagnosis systems are now available online to give users quick and preliminary diagnosis information. The need for such a system has become particularly evident in areas with insufficient health professionals. Due to the privacy of patient medical information and the sensitivity of cloud diagnosis models, it is necessary to protect the security of data, models, and communications. These existing diagnosis systems can hardly provide a satisfied diagnosis accuracy while ensuring comprehensive security and high efficiency. In order to solve these problems, we proposed Relief- k minimum Wasserstein distance (Relief- k MW) classification method, which combined data encryption and BLS signature to form a privacy-preserving efficient online multiparty interactive medical prediagnostic scheme (OMPD). Theoretical analysis shows our OMPD effectively provides high-precision prediagnosis services. Extensive experimental results demonstrate that OMPD not only greatly improves the diagnostic accuracy but also reduces the computational and communication overhead.
Highlights
With the rapid development of mobile Internet, wearable devices, and intelligent Internet of Things, online medical prediagnosis systems that can provide prediagnosis services and medical advice anytime and anywhere have received extensive research attention due to their importance
An online medical prediagnosis system needs to provide a high degree of diagnostic accuracy along with a strong level of privacy protection
The results show that the classification accuracy of online multiparty interactive medical prediagnostic scheme (OMPD) is significantly higher than the other three schemes
Summary
With the rapid development of mobile Internet, wearable devices, and intelligent Internet of Things, online medical prediagnosis systems that can provide prediagnosis services and medical advice anytime and anywhere have received extensive research attention due to their importance. We need an efficient information security scheme for online medical prediagnosis system to protect the communication. For this target, there are some existing methods [16,17,18,19], such as secure multiparty protocol, digital signature, and some other data encryption methods. We proposed an online multiparty interactive medical prediagnosis service scheme with high efficiency, high precision, and privacy protection, called OMPD. It can protect the private information of medical users, a large of medical instances of the hospital and the diagnosis model in the cloud.
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