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Secure Transformation Based Approach For Outsourced Image
Reconstruction Service
M. Jeevitha Lakshmi S. UmapriyaR.
RamyaM. SivaSindhu
Department of Electronics and Communication Engineering, Dr.S.J.S. Paul Memorial College of
Engineering and Technology, Pondicherry University,India.
ABSTRACT Now-a-days image or data is not retrieve properly in cloud because large number
of problem is created, from this the data may losses. So, we choose OIRS under the compressed
sensing framework, which is known for its simplicity of unifying the traditional sampling and
compression for image... leia mais acquisition. Data owner only need to outsource compressed image
samples to cloud for reduced storage overhead.
OIRS provides security, efficiency and it also
reduce design complexity. In OIRS design the sparse image is taken because, it takes less
memory in the database memory. By using this technique the retrieved image becomes accuracy
and efficiency.
The data users can easily reconstruct the original image without any loss.
Key terms sparse image, compressed sensing, security and efficiency, cloud computing.
I. INTRODUCTION
A specialized field in computer networking
that involves securing a computer network
infrastructure. Network security is typically
handled by a network administrator or
system administrator who implements the
security policy, network software and
hardware needed to protect a network and
the resources accessed through the network
from unauthorized access and also ensure
that employees have adequate access to the
network and resources to work. The need for
network security is to protect vital
information while still allowing access to
those who need it example trade secrets,
medical records, etc and it provide
authentication and access control for
resources example Andrew File System.
Network defenses are constantly under
attack from cyber criminals, organized
hacktivists, and even disgruntled ex-
employees. With the advancement of
information and computing technology,
large-scale datasets are being exponentially
generated today. Examples under various
application contexts include medical images,
remote sensing images [2],satellite image
databases, etc.
Along with such data
explosion is the fast-growing vogue to
outsource the image management systems to
cloud and leverage its economic yet lavish
computing resources to efficiently and
effectively acquire, store, and share images
from data owners to a large number of data
users.
Although outsourcing the image services is
quite promising, in order to become truly
successful, it still faces a number of
fundamental and critical challenges, among
which security is the top item. This is due to
the fact that the cloud is an open
environment operated by external third
parties who are usually outside of the data
owner/users' trusted domain [12], [17]. On
the other hand, many image datasets, e.g.,
The medical images with diagnostic results
for different patients, are privacy-sensitive
by its nature [28].Thus, it is of critical
importance to ensure that security must be
embedded in the image service outsourcing
design from the very beginning.
Reconstructing images from compressed
samples requires solving an optimization
problem, it can be burdensome for users
with computationally weak devices, like
tablets or large-screen smart phones. OIRS
aims to shift such expensive computing
workloads from data users to cloud for faster
image reconstruction and less local resource
consumption, yet without introducing
undesired privacy leakages on possibly
sensitive image samples or the recovered
image content.
To meet these challenging
requirements, a core part of the OIRS design
is a tailored light weight problem
transformation mechanism, which can help
data owner/user to protect the sensitive data
contained in the optimization problem for
original image reconstruction.
Video and image applications require
intensive data acquisition, storage, and
processing in order to transmit high quality
images through limited bandwidth. Due to
the large amount of data to be processed and
the limitation in storage and processing
time, image compression algorithms are
exploited to reduce the amount of image
data.
There are many image compression
algorithms which exploit the sparsity of a
transformed image in some particular
domains like wavelet or DWT transforms.
These algorithm is any wavelet transform
for which the wavelets are discretely
sampled. As with other wavelet transforms,
a key advantage it has over Fourier
transforms is temporal resolution: it captures
both frequency and location information
(location in time).
Compressive Sensing (CS) is a method
which addresses this problem. In this
method, instead of performing coding
process after image acquisition, image
compression and acquisition are performed
at the same time. The compression process
is nothing but taking some random
measurements of the image.
On the other
hand, inherent properties of images are
exploited to recover the image from its
measurements in the reconstruction stage by
solving some convex optimization problems.
Here the investigation for these challenges
and propose a novel outsourced image
recovery service (OIRS) architecture with
privacy assurance. For the simplicity of data
acquisition at data owner side, OIRS is
specifically designed under the compressed
sensing framework.
The acquired image
samples from data owners are later sent to
cloud, which can be considered as a central
data hub and is responsible for image
sample storage and provides on-demand
image reconstruction service for data users.
Because reconstructing images from
compressed samples requires solving an
optimization problem [11], it can be
burdensome for users with computationally
weak devices, like tablets or large-screen
smart phones. OIRS aims to shift such
expensive computing workloads from data
users to cloud for faster image
reconstruction and less local resource
consumption, yet without introducing
undesired privacy leakages on the possibly
sensitive image samples or the recovered
image content.
To meet these challenging
requirements, a core part of the OIRS design
is a tailored lightweight problem
transformation mechanism, which can help
data owner/user to protect the sensitive data
contained in the optimization problem for
original image reconstruction. Cloud only
sees a protected version of the compressed
sample, solves a protected version of the
original optimization problem, and outputs a
protected version of the reconstructed
image, which can later be sent to data
user/owner for easy local post processing.
Compared to directly reconstructing the
image locally, OIRS is expected to bring
considerable computational savings to the
owner/users.
As another salient feature,
OIRS also has the benefit of not incurring
much extra computational overhead on the
cloud side. colapsar
Reconstruction Service
M. Jeevitha Lakshmi S. UmapriyaR.
RamyaM. SivaSindhu
Department of Electronics and Communication Engineering, Dr.S.J.S. Paul Memorial College of
Engineering and Technology, Pondicherry University,India.
ABSTRACT Now-a-days image or data is not retrieve properly in cloud because large number
of problem is created, from this the data may losses. So, we choose OIRS under the compressed
sensing framework, which is known for its simplicity of unifying the traditional sampling and
compression for image... leia mais acquisition. Data owner only need to outsource compressed image
samples to cloud for reduced storage overhead.
OIRS provides security, efficiency and it also
reduce design complexity. In OIRS design the sparse image is taken because, it takes less
memory in the database memory. By using this technique the retrieved image becomes accuracy
and efficiency.
The data users can easily reconstruct the original image without any loss.
Key terms sparse image, compressed sensing, security and efficiency, cloud computing.
I. INTRODUCTION
A specialized field in computer networking
that involves securing a computer network
infrastructure. Network security is typically
handled by a network administrator or
system administrator who implements the
security policy, network software and
hardware needed to protect a network and
the resources accessed through the network
from unauthorized access and also ensure
that employees have adequate access to the
network and resources to work. The need for
network security is to protect vital
information while still allowing access to
those who need it example trade secrets,
medical records, etc and it provide
authentication and access control for
resources example Andrew File System.
Network defenses are constantly under
attack from cyber criminals, organized
hacktivists, and even disgruntled ex-
employees. With the advancement of
information and computing technology,
large-scale datasets are being exponentially
generated today. Examples under various
application contexts include medical images,
remote sensing images [2],satellite image
databases, etc.
Along with such data
explosion is the fast-growing vogue to
outsource the image management systems to
cloud and leverage its economic yet lavish
computing resources to efficiently and
effectively acquire, store, and share images
from data owners to a large number of data
users.
Although outsourcing the image services is
quite promising, in order to become truly
successful, it still faces a number of
fundamental and critical challenges, among
which security is the top item. This is due to
the fact that the cloud is an open
environment operated by external third
parties who are usually outside of the data
owner/users' trusted domain [12], [17]. On
the other hand, many image datasets, e.g.,
The medical images with diagnostic results
for different patients, are privacy-sensitive
by its nature [28].Thus, it is of critical
importance to ensure that security must be
embedded in the image service outsourcing
design from the very beginning.
Reconstructing images from compressed
samples requires solving an optimization
problem, it can be burdensome for users
with computationally weak devices, like
tablets or large-screen smart phones. OIRS
aims to shift such expensive computing
workloads from data users to cloud for faster
image reconstruction and less local resource
consumption, yet without introducing
undesired privacy leakages on possibly
sensitive image samples or the recovered
image content.
To meet these challenging
requirements, a core part of the OIRS design
is a tailored light weight problem
transformation mechanism, which can help
data owner/user to protect the sensitive data
contained in the optimization problem for
original image reconstruction.
Video and image applications require
intensive data acquisition, storage, and
processing in order to transmit high quality
images through limited bandwidth. Due to
the large amount of data to be processed and
the limitation in storage and processing
time, image compression algorithms are
exploited to reduce the amount of image
data.
There are many image compression
algorithms which exploit the sparsity of a
transformed image in some particular
domains like wavelet or DWT transforms.
These algorithm is any wavelet transform
for which the wavelets are discretely
sampled. As with other wavelet transforms,
a key advantage it has over Fourier
transforms is temporal resolution: it captures
both frequency and location information
(location in time).
Compressive Sensing (CS) is a method
which addresses this problem. In this
method, instead of performing coding
process after image acquisition, image
compression and acquisition are performed
at the same time. The compression process
is nothing but taking some random
measurements of the image.
On the other
hand, inherent properties of images are
exploited to recover the image from its
measurements in the reconstruction stage by
solving some convex optimization problems.
Here the investigation for these challenges
and propose a novel outsourced image
recovery service (OIRS) architecture with
privacy assurance. For the simplicity of data
acquisition at data owner side, OIRS is
specifically designed under the compressed
sensing framework.
The acquired image
samples from data owners are later sent to
cloud, which can be considered as a central
data hub and is responsible for image
sample storage and provides on-demand
image reconstruction service for data users.
Because reconstructing images from
compressed samples requires solving an
optimization problem [11], it can be
burdensome for users with computationally
weak devices, like tablets or large-screen
smart phones. OIRS aims to shift such
expensive computing workloads from data
users to cloud for faster image
reconstruction and less local resource
consumption, yet without introducing
undesired privacy leakages on the possibly
sensitive image samples or the recovered
image content.
To meet these challenging
requirements, a core part of the OIRS design
is a tailored lightweight problem
transformation mechanism, which can help
data owner/user to protect the sensitive data
contained in the optimization problem for
original image reconstruction. Cloud only
sees a protected version of the compressed
sample, solves a protected version of the
original optimization problem, and outputs a
protected version of the reconstructed
image, which can later be sent to data
user/owner for easy local post processing.
Compared to directly reconstructing the
image locally, OIRS is expected to bring
considerable computational savings to the
owner/users.
As another salient feature,
OIRS also has the benefit of not incurring
much extra computational overhead on the
cloud side. colapsar
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