06-09-2016, 12:52 PM
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Abstract
Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile communications and cloud computing technologies to provide feedback decision support, has been considered as a revolutionary approach to improving the quality of healthcare service while lowering the healthcare cost. Unfortunately, it also poses a serious risk on both clients’ privacy and intellectual property of monitoring service providers, which could deter the wide adoption of mHealth technology. This paper is to address this important problem and design a cloud assisted privacy preserving mobile health monitoring system to protect the privacy of the involved parties and their data. Moreover, the outsourcing decryption technique and a newly proposed key private proxy re-encryption are adapted to shift the computational complexity of the involved parties to the cloud without compromising clients’ privacy and service providers’ intellectual property. Finally, our security and performance analysis demonstrates the effectiveness of our proposed design.
Existing System
Existing Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile communications and cloud computing technologies to provide feedback decision support, has been considered as a revolutionary approach to improving the quality of healthcare service while lowering the healthcare cost. Unfortunately, it also poses a serious risk on both clients’ privacy and intellectual property of monitoring service providers, which could deter the wide adoption of mHealth technolog
Proposed System
CAM consists of four parties: the cloud server (simply the cloud), the company who provides the mHealth monitoring service (i.e., the healthcare service provider), the individual clients (simply clients), and a semi-trusted authority (TA). The company stores its encrypted monitoring data or program in the cloud server. Individual clients collect their medical data and store them in their mobile devices, which then transform the data into attribute vectors. The attribute vectors are delivered as inputs to the monitoring program in the cloud server through a mobile (or smart) device. A semi-trusted authority is responsible for distributing private keys to the individual clients and collecting the service fee from the clients according to a certain business model such as pay-as-you-go business model. The TA can be considered as a collaborator or a management agent for a company (or several companies) and thus shares certain level of mutual interest with the company. However, the company and TA could collude to obtain private health data from client input vector
Conclusions
we design a cloud-assisted privacy preserving mobile health monitoring system, called CAM, which can effectively protect the privacy of clients and the intellectual proerty of mHealth service providers. To protect the clients’ privacy, we apply the anonymous Boneh-Franklin identitybased encryption (IBE) in medical diagnostic branching programs. To reduce the decryption complexity due to the use of IBE, we apply recently proposed decryption outsourcing with privacy protection to shift clients’ pairing computation to the cloud server. To protect mHeath service providers’ programs, we expand the branching program tree by using the random permutation and randomize the decision thresholds used atthe decision branching nodes. Finally, to enable resourceconstrained small companies to participate in mHealth business, our CAM design helps them to shift the computational burden to the cloud by applying newly developed key private proxy re-encryption technique. Our CAM has been shown to achieve the design objective.