ABSTRACT
Data mining is a useful tool for extracting nontrivial, implicit,
previously unknown and potentially useful information or pattern from large
databases. Such large databases are possibly store at different places over
network or may store at one place. This paper use association rule mining to
exploring interesting relationship between data. While exploring useful
information, needs to concentrate on that it should not expose any private
data to preserve privacy. This paper based on two concepts: SMC and
association rule hiding. Basically SMC (Secure Multi-party Computation)
deals with secure data transfer over the network and hiding process hide
sensitive association rule which create threat to privacy.
EXISTING SYSTEM:
The existing system no party can find out input from other party.
There are many real world problems exist which cannot be viewed as SMC
and cannot be solved by using SMC
PROPOSED SYSTEM:
The system proposed in this paper is based on two concepts. One of
them is Secure Multiparty Computation and second one is association rule
hiding. It is based on trusted third party model of SMC and it is divided
mainly into three parts.
 First part related to the secure dataset collection from each party
involve in secure computation.
 Second part is used to generate all association rule of collected
dataset.
 Final third part is about to hide the association rule by using hybrid
algorithm which combination of both ISL and DSR technique.
MODULES
1. Secure Multi-party Computation Module.
2. Association rule generation Module.
3. Dataset collection Module.
4.
Hiding Association rule Module.
Secure Multi-party Computation Module:
There are many real world problems exist which can be viewed as SMC
and can be solved by using SMC. The SMC may be based on one of model:
A. Without Third party: Parties can run and use their own SMC protocols
without the need of third party for joint computation.
B. Trusted Third Party: Parties rely on trusted third party for joint
computation.
Association rule generation Module:
As dataset collected and changing sequence order of dataset record by
Disturb center made available to Data miner. Dataset available at Data miner is
still encrypted with central encryption key E is decrypted by Data miner using
central decryption key D getting the combined and mixed dataset. After that
Data miner simply apply Apriori algorithm on dataset to get frequent item set
and hence to find out association rules A→B contains which satisfy the two
conditions:
1.
≥ min_support
2.
≥ min_confidence
Where
n – is number of record available
Min_support and min_confidence – are user defined threshold values
Dataset collection Module:
In dataset collection step, we use both private and public encryption
technique. We use private encryption technique at Disturb center and public
encryption technique at Data miner. First Data miner produce central
encryption key E and key pair (ei, di) for each party on network. Data miner
send central encryption key E and party encryption key ei to each party and
also send di to the Disturb center. Simultaneously Disturb center provide
identification i.e. ID to each site.
Consider at party X, X receive the key pair (E and eA) from Data miner and
ID from Disturb center. Party X, first encrypt dataset with central encryption
key and then encrypt with unique encryption key e x. Again party X uses ID to
encrypt dataset which is already encrypted twice. Then party X, send this data
to the Disturb center. At Disturb center, received encrypted dataset of each
party is first decrypted using ID and di. After this Disturb center change order
dataset records and send this whole collected data set to the Data miner for
further operation.
Hiding Association rule Module:
To decrease the confidence of a rule A→B, we can either increase the
support of A, i.e., the left hand side of the rule, but not support of (A⋃B), or
decrease the support of the item set (A⋃B). For the second case, if we only
decrease the support of Y, the right hand side of the rule, it would reduce the
confidence faster than simply reducing the support of (A⋃B). To decrease
support of an item, we will modify one item at a time by changing from 1 to 0 or
from 0 to 1 in a selected transaction. Hybrid algorithm first tries hide rules in
which item A in RHS and then tries to hide rules in which item in LHS.
SYSTEM SPECIFICATION
Hardware Requirements:
• System
: Pentium IV 2.4 GHz.
• Hard Disk
: 40 GB.
• Floppy Drive
: 1.44 Mb.
• Monitor
: 14’ Colour Monitor.
• Mouse
: Optical Mouse.
• Ram
: 512 Mb.
• Keyboard
: 101 Keyboard.
Software Requirements:
• Operating system : Windows XP.
• Coding Language : ASP.Net with C# SP1.
• Data Base
: SQL Server 2005.