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Thursday, 22 February 2018

CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring (2013)

CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring (2013)

 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.

IMPLEMENTATION


Implementation is the stage of the project when the theoretical design is turned out into a working system. Thus it can be considered to be the most critical stage in achieving a successful new system and in giving the user, confidence that the new system will work and be effective.
          The implementation stage involves careful planning, investigation of the existing system and it’s constraints on implementation, designing of methods to achieve changeover and evaluation of changeover methods.

MODULE DESCRIPTION:

Branching Program:
             we formally describe the branching programs, which include binary classification or decision trees as aspecial case. We only consider the binary branching program for the ease of exposition since a privatequery protocol based on a general decision tree can be easily derived from our scheme. Let v be the vector of clients’ attributes. To be more specific, an attribute component vis a concatenation of an attribute index and the respective attribute value. For instance, A||KW1 might correspond to “blood pressure: 130”. Those with a blood pressure lower than 130 are considered as normal, and those above this threshold are considered as high blood pressure.  The first element is a set of nodes in the branching tree. The non-leaf node pis an intermediate decision node while leaf node pis a label node. Each decision node is a pair (ai, ti), where ais the attribute index and tis the threshold value with which vais compared at this node. The same value of amay occur in many nodes, i.e., the same attribute may be evaluated more than once. For each decision node iL(i) is the index of the next node if vai  ≤ tiR(i) is the index of the next node if va> ti. The label nodes are attached with classification information. Repeat the process recursively for ph, and so on, until one of the leaf nodes is reached with decision information.

Token Generation:
              To generate the private key for the attribute vector v=(v1· · · , vn), a client first computes the identity representation set of each element in v and delivers all the identity representation sets to TA. Then TA runs the AnonExtract(idmsk) on each identity id ∈ Svin the identity set and delivers all the respective private keys skvto the client.

Query:
               A client delivers the private key sets obtained from the TokenGen algorithm to the cloud, which runs the AnonDecryption algorithm on the ciphertext generated in the Store algorithm. Starting from p1, the decryption result determines which ciphertext should be decrypted next. For instance, if v∈ [0, t1], then the decryption result indicates the next node index L(i). The cloud will then use skv(L(i)) to decrypt the subsequent ciphertext CL(i). Continue this process iteratively until it reaches a leaf node and decrypt the respective attached information.

Semi Trusted Authority:
                       
            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 vectors.

System Configuration:-

H/W System Configuration:-


        Processor               -    Pentium –III


Speed                                -    1.1 Ghz
RAM                                 -    256  MB(min)
Hard Disk                          -   20 GB
Floppy Drive                     -    1.44 MB
Key Board                         -    Standard Windows Keyboard
Mouse                                -    Two or Three Button Mouse
Monitor                              -    SVGA


 S/W System Configuration:-

v   Operating System            :Windows95/98/2000/XP
v   Application  Server          :   Tomcat5.0/6.X                                                  
v   Front End                          :   HTML, Java, Jsp
v    Scripts                                :   JavaScript.
v   Server side Script             :   Java Server Pages.
v   Database                            :   Mysql
v   Database Connectivity      :   JDBC.

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