Privacy Preserving Made Practical with Cloud Computing
Back propagation & neural network learning
Deepansu Singh1, Suraj
Satav1
Prof Mohsin Mulla2
Students
Department of Computer
Engineering, GHRCEM
Pune
Department of Computer
Engineering, GHRCEM
Pune
mmoseen87@gmail.com2
deepf15@gmail.com, 1
surajsatav30891@gmail.com
Abstract: Privacy preserving in cloud is very difficult in
vertical approach for privacy preserving BPN network
today aspect. Cloud is mainly used for larger data
storage. Data Sharing is important in cloud storage. In
this paper, we are going to describe how efficiently and
flexibly one can share data and preserve it into the
cloud. Back Propagation neural networks help to train
data and load data and finally to improve the accuracy
of result. During the process, when multiple parties may
join through conducting back-propagation neural
network, no party wants to expose their own private
data to others. Existing models support this kind of
collaborative learning and limited in the way of data
partitioning or just only for two parties. There is a
problem, it just only for two parties. In our proposed
scheme, each party encrypts his/her own private data
locally & uploads ciphertexts into the cloud.
learning algorithm just only for two parties. Cloud
Keywords
storage is responsible for storing and keeping the data
available and accessible. The main concern is how to
share those encrypted data which were done at client
site. One can download the shared encrypted data from
server &decrypt them. ANN is used to train the data at
the beginning stage and apply BPNN to approximate
value of data. BPNN is used for error correction.
Scaling and transformation is used for encryption of
data. In our scheme privacy preserving in cloud
containing following functionalities:

In cloud we are going to provide privacy to
data at both client and server site after doing
Privacy preserving, Back-propagation neural
network (BPNN), Artificial neural network (ANN),
Cloud computing.
encryption at both the sides.

At the client site data encrypted with the help
of transformation and scaling.
INTRODUCTION

And we are going to train the data at the server
site with the help of ANN and minimize the
Privacy preserving is very important from the today’s
error with the help of BPNN and get
world’s aspect, and in cloud it is very difficult to
approximate result.
provide privacy to data. Due to this an encryption and

Cloud is helpful in storage of sensitive data
decryption technique is developed to preserve the data
such as Government data etc. we provide
and provide privacy to them. [1] Introduction to
privacy with our scheme.
horizontal approach for privacy preserving (BPN)

In cloud privacy is very important so, at server
network learning scheme. That enables two or more
site original data is transformed into another
than two parties together perform BPN network
and sent to client.
learning without disclosing their data. [2] Introduces



Then after client decrypt the data and generate
using BPNN with cloud computing in section V we
the result.
have discussed the proposed solutions for Privacy
Back-propagation provides training to the data
preserving using BPNN with cloud computing in
just like ANN. But it had two stages1) Feed
section VI we have highlighted the encryption and
forward, 2) Feed backward.
decryption in our project and finally in Section VII
In cloud, loss of data is very common term,
conclusion and Section VIII Future work.
that’s why we provide encryption to the data
for no data losing.
I. Artificial Neural Network
[Our contribution] In this work, we address this open
problem by incorporating the computing power of the
cloud. The main idea of our scheme can be summarized
as follows: is participant first encrypts her/his private
data with the system public key and then uploads the
cipher text to the cloud; the participants jointly decrypt
the results with which they update their respective
weights for the BPN network. During this process cloud
servers learn no privacy data of a participant even if
they collide with all the rest participants. Through
offloading the computation task to the resourceabundant
cloud,
our
computation/communication
scheme
makes
complexity
on
the
each
participant independent to the number of participants
and each thus, highly scalable. For privacy preservation,
we decompose most of the sub algorithms of BPNN
into simple operations such as addition, multiplication
and scalar product. As decryption of BGN is limited to
small numbers, we introduce a novel design in our
scheme such that arbitrarily large numbers can be
efficiently decrypted.
Artificial neural network is very useful for training of
data it work similarly as the brain does. It is composed
of a large number of interconnected nodes working
together to solve the specific problems. ANN provides
training of data at the beginning stage where the data is
totally blank. ANN is used for a specific application,
such as pattern recognition or speech recognition or
data classification through learning process. Learning is
a biological process which involves connection that
exists between the neurons or neural cells. An artificial
neuron is a neuron which consists of many inputs and
one output. ANN work as similar as human brain does.
There are following four advantages
: 1) Adaptive
learning 2) Self-organization 3) Real time operation 4)
Fault-tolerance.
The neurons as shown in fig. a. had two modes of
operation, the training mode and using mode. In
training mode, the neurons can be trained to fire for
particular input patterns. In the using mode, when an
input pattern is detected at the input, its associated
output becomes the current output.
Neural network process information in a similar way
In this paper we are detailing various flavors of Privacy
preserving using Back-propagation Neural Network
with cloud computing and their existing and proposed
the human brain does. The network is composed of a
large number of highly interconnected processing
elements working in parallel to solve specific problem.
solution, in section I we have discussed Artificial neural
network in Section II we have discussed Backpropagation neural network in Section III we have
discussed about Cloud computing in Section IV we
have discussed existing solutions for Privacy preserving
Use
X1
X2
Neuron
2
2
Inputs
Outputs
.
Xn
Teaching inputs
N
N
N
Input
Hidden
Output
Fig. b. BPNN
Fig. a. Neuron
As shown in fig. b, back propagation neural networks
used in all three layers of neurons, or nodes. Each node
of input and hidden layer is connected to each other.
And the nodes in the next layer i.e. hidden or output are
II. Back-Propagation Neural Network
Back propagation neural network algorithm is mainly
used to reduce the error with having threshold value
with them. It is a multi-layered feed forward or feed
backward network trained according to error backpropagation algorithm. It is one of the most widely used
and applied neural networks model. Back-propagation
is mainly used for learning and for considering to
solving problems with many inputs and outputs. Its
learning rule is to adapted in which the back
connected with previous connected nodes i.e. input and
hidden. All the connections between nodes are directed
means the information flows only one way, and there
are no connections between nodes within a particular
layer. Each connection between nodes has a weighing
factor or having the threshold value associated with it.
These weights are modified using the BPNN algorithm
during the training process, and threshold value is used
to minimize the error and provide appropriate result
using BPNN algorithm
propagation is used to regulate the threshold value &
the weight value and of the network to achieve the
III. Cloud
minimum error. It has been used successfully for wide
variety of application such as speech or voice
Cloud is used for larger data storage and for large
recognition,
medical
number of transferring of data from client to server or
diagnosis and automatic controls. When each entry of
server to client. Cloud storage is useful for storing
the trained data or sample set is presented to the
sensitive data i.e. Government data etc. Cloud storage
network, the network reads the inputs responds to the
providers are responsible for storing, keeping the data
sample input patterns. The output response is calculated
available and accessible. Cloud storage is gaining
and compared with other outputs to minimize the error
popularity recently. In enterprise has been an abrupt rise
value. On the basis of error, the connection weight and
in demand for data outsourcing, which serves in
adjusted.
management of co-operate data.
image,
Pattern
recognition,
Data sharing is important term in cloud. The main
concern is how to share those encrypted data. One can
download the shared encrypted data from server &
1
1
1
decrypt them. With the wireless technology, one can
access with an ease all their files and emails through
TPA performs six types of steps on the received
any network based devices whether it is mobile or
file/files from the respective client for storage across
computer. Sharing become even worse, in cloud
cloud with security and availability.
computing environment. Data is stored in single
physical machine and sharing of data is done from that
a) Received file
site only. Due to this cloud users may not hold a good
Third party auditor received from the client/clients.
trust that there data on the cloud server is secure from
interloper. Here users are allowed to encrypt their data
b) Partitioning file
on the client side with their encryption keys before
TPA file partition received from the client/clients.
uploading data in to the cloud.
c) Digital signature extraction
TPA extract digital signature from each file partition.
IV.
Existing Solutions:
d) Secret key generation
After partitioning, third party auditor generate secret
key of each partition respectively.
Cloud Server
e) Encryption
Third Party Auditor
TPA encrypts each partition using respective secret
Data flow
key/keys.
Cloud Server Provider
User
3. Server side:
Service Message Flow
a) Sending partition
Third party auditor sends the respective partition to the
Fig. c. partitioning and domain integrity
checking for data storage in cloud computing
respective storage.
b) Storing
The existing system represents how data is stored and
The storage server stores the partition received from the
distributed on the cloud using partitioning to provide
TPA.
security and availability across two servers.
Work Flow:
V. Proposed Scheme:
1. Client Side:
a) File Selection:
Fig. d. shows the proposed system. The system consist
The user/users select a file to upload on the server.
of end users, cloud server on which data to be stored.
b) Sending File
1) The user can login on his system.
the client/clients sends respective file to third party
2) After login he can send data to the server which is
auditor(TPA) across the network using a file transfer
training data.
protocol(FTP).
3) After receiving the data and ANN parameter by
2. TPA:
server using this data ANN server will trained.
4) Then he will waiting for test data from the user. Then
user loads the test data.
the help of transformation and scaling, and send the
5) The test data is received to the server then server can
data at the server site. Then after this server once more
apply privacy preserving BPNN on the data. Then this
encrypt the data (i.e. double encryption performed)
data is stored to that server.
when client asked for data. At the server site training of
6) User can retrieve this encoded data and decode
data using key.
data has been done with the help of ANN (Artificial
Neural Network) and back-propagation is doing several
times for the original data. There after encrypted data
sent to the client. At the client site decryption of data is
Server
done and generates the result.
B.P.N.N
6. Data Received
Glass Fish
Database
Web Services
7. ANN parameter
received
8. Train using ANN
9. Wait for the test
data received
VII. Conclusion
In this paper we proposed fully homomorphic
encryption system for the data storage security in the
cloud computing. We have integrated the fully
12. Test data
SOAP/XML
13. Apply privacy
preserving BPNN
homomorphic encryption in TPA (Third Party Auditor)
system. Cloud data storage networks include a Third
Party Auditor which has the power and capabilities that
14. Result
a client does not have. The problem of data security and
integrity has been presented. The proposed system, we
1. Login (using DSA)
purposed the first secure and practical multiparty BPN
2. Load training data
network learning scheme over arbitrarily partitioned
3. Transform Data
data. In the purposed system, the parties encrypt their
arbitrarily partitioned data and upload the cipher texts to
4. Encoded data using
key
the clouds. The cloud can execute most operation
5. Submit to server
pertaining to the BPN network learning algorithm
10. Load test data &
transform
11. Encode using keys
and submit
15. Receive results
without knowing any private information. Cost of each
party in our scheme is independent to the no of parties.
Complexity and security analysis shows that our
purposed scheme is scalable, efficient and secure. We
provide encryption at both client and server site
16. Decode results using
keys
17. Done
Fig. d. Privacy Preserving Using ANN over Cloud
VIII. Future Work
Our future work is to implement RSA algorithm and
BPNN ANN algorithm to improve privacy of data in
VI.
Encryption And Decryption
cloud storage and compare it with other schemes and
algorithms based on true positive result and with
Firstly we have to encrypt the data at the client site with
various network scenarios and parameters. We are
going to use transformation and scaling at client site for
data encryption and send the encrypted data to server
then their server trains the data and encrypt the data,
then after data sent to client. Then client decrypts the
data and generate the result.
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