Achieve Efficient and Privacy-Preserving Disease Risk Assessment Over Multi-Outsourced Vertical Datasets

It is believed that online disease risk assessment system has great potential to alleviate the medical treatment problems for the future smart city and communities, as it can excavate disease risk factors from a large number of patient features, provide diagnostic references for doctors, and save medical treatment time for patients. However, the flourish of online disease risk assessment service still faces severe challenges including information privacy and security. In this article, based on the naïve Bayesian classification, we propose an effi
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isk assessment scheme over multi-outsourced vertical datasets, named CARER. With CARER, the e-healthcare provider can securely train a disease risk predication model over vertically distributed medical data from multiple medical centers (i.e., hospitals), and provide privacy-preserving disease risk predication services for users (i.e., patients and doctors). During the model training and disease risk prediction phases, all sensitive data are operated over ciphertexts without decryption. As a result, the private information of medical centers, e-healthcare provider, and users can be well protected. Detailed security analysis shows that CARER can resist various known security threats. In addition, we evaluate the performance of CARER with real medical datasets, and the results demonstrate that CARER is efficient.