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:
Input
Hidden Layer
(False
Information)
Output
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.
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 backpropagation 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.
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