E-healthcare system have been increasingly facilitating health condition monitoring, disease modeling and early intervention, and evidence-based medical treatment by medical text mining and image feature extraction. Owing to the resource constraint of wearable mobile devices, it is required to outsource the frequently collected personal health information (PHI) into the cloud. Unfortunately, delegating both storage and computation to the un trusted entity would bring a series of security and privacy issues. The existing work mainly focused on fine-grained a privacy-preserving static medical text access and analysis, which can hardly afford the dynamic health condition fluctuation and medical image analysis. In this paper, a secure and efficient privacy-preserving dynamic medical text mining and image feature e extraction scheme PPDM in cloud-assisted e-healthcare system sis proposed. Firstly, an efficient privacy-preserving fully data aggregation is proposed, which serves the basis for our proposed PPDM. Then, an outsourced disease modeling and early intervention is achieved, respectively by devising an efficient privacy-preserving function correlation matching PPDM1 from dynamic medical text mining and designing a privacy-preserving medical image feature extraction PPDM2. Finally, the formal security proof and extensive performance evaluation demonstrate our proposed PPDM achieves a higher security level (i.e. information-theoretic security for input privacy and adaptive chosen cipher text attack (CCA2) security for output privacy) in the honest but curious model with optimized efficiency advantage over the state-of-the-art in terms of both computational and communication overhead.
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