This document presents a privacy-preserving algorithm for back-propagation neural network learning with data arbitrarily partitioned between two parties, addressing privacy concerns when sharing training datasets. The proposed method ensures no knowledge is leaked about one party's data, besides the final learned outcomes, while achieving efficiency in computation and communication. This work marks a significant advancement in secure neural network training, especially with regards to data privacy.

1. Privacy Preserving Back-Propagation Neural Network Learning Over
Arbitrarily Partitioned Data
Abstract:
Neural Networks have been an active research area for decades. However, privacy bothers
many when the training dataset for the neural networks is distributed between two parties,
which are quite common nowadays. Existing cryptographic approaches such as secure scalar
product protocol provide a secure way for neural network learning when the training dataset is
vertically partitioned. In this paper we present a privacy preserving algorithm for the neural
network learning when the dataset is arbitrarily partitioned between the two parties. We show
that our algorithm is very secure and leaks no knowledge (except the final data’s learned by
both parties) about other party’s data. We demonstrate the efficiency of our algorithm by
experiments on real world data.
Architecture:
Neural Network:
GLOBALSOFT TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
Input OutputHidden Layer
(False
Information)

2. Existing System:
Existing approaches such as not secure scalar product protocol provide a
secure way for neural network learning when the training dataset is partitioned.
Disadvantages:
To the best of our knowledge the problem of privacy preserving neural network
learning over arbitrarily partitioned data has not been solved.
Proposed System:
In this paper we propose a privacy preserving algorithm for back-
propagation neural network learning when the data is arbitrarily partitioned. Our contributions
can be summarized as follows.
(1) To the best of our knowledge we are the first to propose privacy preserving for the neural
networks when the data is arbitrarily partitioned.
(2) It is quite efficient in terms of computational and communication overheads.
(3) In terms of privacy, leaks no knowledge about other’s party data except the final data’s.
Advantages:
To the best of our knowledge the problem of privacy preserving neural network
learning over arbitrarily partitioned data has been solved.

3. Algorithm:
Privacy preserving Algorithm:
It is highly important that not only the data but the in-
termediate data’s also should not be revealed to the other party because intermediate data’s
contain partial knowledge about the data. We propose an algorithm in which both parties
modify the data’s and hold random shares of the data’s during the training. Both the parties use
the secure 2-party computation.
Modules:
1. Arbitrary Partitioned Data
2. Homomorphic Encryption
3. Privacy Preserving Learning
1. Arbitrary Partitioned Data:
We consider arbitrary par- titioning of data between two
parties in this paper. In arbitrary partitioning of data between two parties, there is no
specific order of how the data is divided between two parties. Combined data of two
parties can be seen as a database.
When the training data for the neural networks is
arbitrarily partitioned between two parties, both parties want to train the network but at
the same time they do not want that the other party should learn anything about its data
except the final data’s learned by the network. So we propose a privacy preserving back-
propagation neural network learning algorithm for the arbitrarily partitioned data between
two parties.
2. Homomorphic Encryption:
Homomorpic property is a property of certain encryption algorithms where
specific algebraic operations can be performed on plaintext by performing the operations on
encryption messages without actually decrypting them. For example say we have two messages
m1 and m2, the encryption of message is denoted by E(m1) and E(m2) then operation m1m2
can be performed using E(m1) and E(m2) only without actually de- crypting the two messages.

4. 3. Privacy Preserving Learning:
This guarantees more security and privacy against the intrusion by the other party.
Data providers for machine learning are not willing to train the neural network with their data at
the expense of privacy and even if they do participate in the training they might either remove
some information from their data or can provide false information.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 512 Mb.
Software Requirements:
• Operating system : - Windows 8.
• Coding Language : C#.net
• Data Base : SQL Server 2008