CAM: Cloud-Assisted
Privacy Preserving Mobile Health Monitoring
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:
Traditional privacy protection mechanisms by simply
removing clients’ personal identity information (such as names or SSN) or by
using anonymization technique fails to serve as an effective way in dealing
with privacy of mHealth systems due to the increasing amount and diversity of
personal identifiable information.
Traditionally, the privacy issue is tackled with anonymization
technique such as k-anonymity or l-diversity. However, it has
been indicated that these techniques might be insufficient to prevent
re-identification attack
DISADVANTAGES
OF EXISTING SYSTEM:
Unfortunately, although cloud-assisted mHealth
monitoring could offer a great opportunity to improve the quality of healthcare
services and potentially reduce healthcare costs, there is a stumbling block in
making this technology a reality. Without properly addressing the data
management in an mHealth system, clients’ privacy may be severely breached during
the collection, storage, diagnosis, communications and computing.
Another major problem in addressing security and
privacy is the computational workload involved with the cryptographic techniques.
With the presence of cloud computing facilities, it will be wise to shift
intensive computations to cloud servers from resource-constrained mobile
devices. However, how to achieve this effectively without compromising privacy
and security become a great challenge, which should be carefully investigated.
PROPOSED SYSTEM:
In this paper, we design a cloud-assisted mHealth
monitoring system (CAM). We first identify the design problems on privacy
preservation and then provide our solutions. To ease the understanding, we
start with the basic scheme so that we can identify the possible privacy
breaches. We then provide an improved scheme by addressing the identified privacy
problems. The resulting improved scheme allows the mHealth service provider
(the company) to be offline after the setup stage and enables it to deliver its
data or programs to the cloud securely. To reduce clients’ decryption
complexity, we incorporate the recently proposed outsourcing decryption technique
into the underlying multi-dimensional range queries system to shift clients’
computational complexity to the cloud without revealing any information on
either clients’ query input or the decrypted decision to the cloud. To relieve the
computational complexity on the company’s side, which is proportional to the
number of clients, we propose a further improvement, leading to our final
scheme. It is based on a new variant of key private proxy re-encryption scheme,
in which the company only needs to accomplish encryption once at the setup
phase while shifting the rest computational tasks to the cloud without
compromising privacy, further reducing the computational and communication
burden on clients and the cloud
ADVANTAGES
OF PROPOSED SYSTEM:
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 at the decision branching nodes.
ARCHITECTURE:
ALGORITHMS USED:
ü Setup
ü Store
ü TokenGen
and
ü Query
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
·
System : Pentium IV 2.4 GHz.
·
Hard Disk : 40 GB.
·
Monitor :
15 inch VGA Colour.
·
Mouse :
Logitech Mouse.
·
Ram : 512 MB
·
Keyboard :
Standard Keyboard
SOFTWARE REQUIREMENTS:
·
Operating System : Windows XP.
·
Coding Language : ASP.NET, C#.Net.
·
Database :
SQL Server 2005
REFERENCE:
Huang Lin_, Jun Shaoy, Chi
Zhangz, Yuguang Fang_, Fellow, IEEE, “CAM: Cloud-Assisted
Privacy Preserving Mobile Health Monitoring”, IEEE Transcations 2013.