International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number3 - Dec 2014
A Novel Privacy Preserving Mining with Hybrid
Pattern Mining and Key Generation
B. Ajit1,C P V N J Mohan Rao2,Sairam Vakkalanka3
1,3
Final M.Tech Student1, Principal2 ,Assistant Professor3
,Dept of Compute science and engineering, Avanthi Institute of Engineering & Technology - Narsipatnam
Abstract: Association rule mining over horizontal
partitioning data is always an interesting research issue in
the field of knowledge and data engineering. Data holder
forwards the data sets to centralized server, privacy can be
maintained by security protocols, and our security protocol
communicates in terms of subsets from both the data
holders and player as ingredients for secure transmission.
Association rules can be generated at centralized server
efficiently. In this paper we are proposing a privacy
preserving mining approach with Improved Shamir secret
key generation for secure key generation and Bit Wise
Matrix approach.
Index Terms: Association Rule mining,Bit Wise Matrix ,
LaGrange’s polynomial.
I. INTRODUCTION
In general we use two methods which is multi security and
the other is computation of the party that catches all the
data of the database. If there is another one who is secures
the data in other words the third party then it would be easy
for the actor to post their inputs and get the data security
and get the result from him without any doubt of the data
leakage because of the belief on the third party that will
secure the data. But in the absence of such third party or
the secure data is not possible then it is necessity to with
the more secure way of getting data secure is that using the
association rule we can get the data secure
The important part of the protocol is to provide
security of the data by computing the sub protocol over the
group of the private database subsets which are obtained
from different actors which is more expensive to get access
over the data base protection over the other actors not to
make available of the data and also providing the security
of the data. Specifically our protocol does not depend upon
the commutative process of transfer data since the solution
of the process may not be secure to protect the data over
the huge leakage of data over the data storing information.
In this paper we propose a new protocol for security of
mining of the data organization in the horizontal circulated
databases. This protocol is based on the mining algorithm
ISSN: 2231-5381
known as quick distributed mining which is an unsafe
distributed form of the apriori algorithm. The important
parts of the algorithm is considered of two unique parts one
among them are security multi and the other is party
algorithm, among the two methods one calculates the
combination of private subsets in which each are relating
actors and the other method of the algorithm is that player .
our protocol provides the secure with respect to the
protocol it is simpler and suggestively more organized in
the communication and computation.
There is a drawback in the security of the mining
of the data using association rule over the horizontal
distribution of the database to get solve the problem there
are n number of sites to get the similar data base which is
nothing but gathering information of the same type but in
different articles. The purpose is to find the association rule
with minimum support and confidence for the required
database that stores the unified database while doing so it
will reveal the information of the other actor whose data
base is out of security.so we would like to secure the data
in the database not only for individual(within it) but also in
the global manner using the association rule.
II. RELATED WORK
In
the
traditional
association
rule
mining,companies give theirdata to the analyst for finding
the patterns or association rules exist between the items.
Although it is advantageous to achieve sophisticated
analysis on tremendous volumes of data in a cost-effective
way, there exist several serious security issues of the datamining as- a-service paradigm. One of the main security
issues is that the server has access to valuable data of the
owner and may learn sensitive information from it. There is
a loss of corporate privacy. Traditional distributing
algorithm based on apriori, main disadvantage of this
approach is multiple database scan and candidate set
generations
We are proposing an efficient distributed secure mining
mechanism, here data holders forwards the homogeneous
datasets to centralized server, all the data holders maintain
same schema. Association rules can be mined at server end.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number3 - Dec 2014
Players generate a key commutatively to encrypt the item
sets.
Data holders are players computes the locally frequent
items from their individual datasets and reduces the cost of
the dataset. Candidate sets can be generated for every
iteration of the item sets, collection of frequent items can
be extracted from fast distributed mining algorithm.
Players generate a random key and commutative
cipher and encrypt all the item sets, before encryption hash
value can be generated for every individual item set. Every
individual should select their different hash value. Prior
hashing is required for leakage of relation of itemsets and
maintains a hash table for set of hash values. Players add
fake item sets between the item sets.
Data Holder1
Data Holder2
Items can me merged and odd players the key to p1 and
even can be forwarded to p2,p1 combines all odd key by
removing fake vales and p2 combines all by eliminating
the fake values.P1 forwards the permuted list to p2 and p2
forwards permuted list p1,it represents the final list.
III. PROPOSED WORK
In this approach we are proposing a privacy preserving
mining approach with Bit Wise Matrix, it reduces problem
of multiple database scans and candidate set generations by
constructing the Bit Wise Matrix. Data can be integrated
from multiple data holders or players, for secure
transmission or distributed partitioning we are
implementing an improved lagranges’s polynomial
approach for secure key generation for encryption of data
from data holders with triple DES algorithm.
Data Holder1
Cipher Pattern
Cipher Pattern
Cipher Pattern
Encoder/Decoder
Centralized Server
Binary Matrix
Fig1 : Horizontal partitioning Architecture
Every individual data holder or player maintains
their transactions or patterns, in horizontal partitioning,
every data holder forwards their patterns to centralized
server after encryption of patterns which are at individual
end,At centralized server received pattern can be decrypted
with decoder and forwarded to Bit Wise Matrix to extract
frequent pattern from the received patterns.
For experimental purpose we establish connection between
the nodes and Central location (Key generation center)
through network or socket programming, Key can be
generated by using improved LaGrange’s polynomial
equation and key can be distributed to user
ISSN: 2231-5381
Every individual node participates in key generation
process and retrieves key by reconstruction. Encrypt the
datasets by using triple DES and key which is generated by
the LaGrange’s polynomial equation. All encrypted
datasets can be forwarded to centralized location and
decrypted with same symmetric key and forwards to
mining process.
Group key manger receives the registration
request from all the users, and generates a verification
share and forwards to all the requested users for
authentication purpose, generates the key using key
generation process and forwards the points to extraction of
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number3 - Dec 2014
the key from the equation generated by the verification
points.
For key generation protocol, it receives the
verification shares and key as input to construct the
lagranges polynomial equation f(x), which is passed
through (0, key) and verification points, after that group
key manager forwards the points to data owners. Data
owners again reconstruct the key from the verification
points and check the authentication code which is sent by
the group key manager.
When a new user tries to download the file, new
user need not to connect other data owner to decryption of
the file, user connects to the group key manager he will
update the group key and decrypts the files with previous
key again encrypt with new key and updates the new key to
all the data owners.
Data owner initiate the request by sending the
random challenge to the group key manager, as a response
Group key manager sends a secret share, data owner
authenticates and forwards the verification share, data
owner receives the verification shares and generates the
key using Lagrange’s polynomial equation and forwards
the points to data owners for regeneration the key
4. Points(Subset of P points)
1. Request ( Rch)
Node users
2.Response (Sshare)
Group
Key
manager
3.Vshare
Fig2 : Authentication and Key Generation
Rch ----Random challenge
Sshare---Secret share
Vshare----verification share
P={p1,p2…pn}-------points for construction of Lagrange’s equation
Bit Wise Matrix:
type of values, ‘1’ and ‘0’, which means that the
The server or service provider performing association rule
transaction record contains or not the corresponding
mining on cipher database for finding maximum frequent
frequent length-1 itemset. Then it is necessary to calculate
item sets. Thus the research presented a new algorithm of
the number of value 1 in each column and the count of the
mining maximum frequent itemsets first based on the Bit
columns with the same number of value 1
Wise Matrix of frequent length-1 itemsets. The main idea
of the algorithm isto create a Bit Wise Matrix with frequent
length-1 itemsets as row headings and Transaction records
IDs as column headings. In the matrix, there are only two
Itemset
Transaction Records ID
1 2
3
4
5
6
7
8
9
10
Item1
1 1
0
1
0
0
1
0
1
1
Item2
0 1
1
0
1
0
1
0
1
1
Item3
1 0
0
1
1
1
0
1
0
0
Item4
1 0
0
1
0
1
0
1
0
0
Fig 3 :Bit Wise Matrix
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number3 - Dec 2014
Now we can extract frequent patterns from the matrix,
to extract frequent 1 itemset, initially count number of ones
in vertical columns with respect to item, if it matches
minimum threshold values then treat it as frequent item
else ignore, continue same process for 2 itemset, check
whether two items have ‘1’ in their corresponding vertical
columns then increment, continue until all transactions
verified. If total count greater than threshold value then
treat it as frequent item
IV. CONCLUSION
We have been concluding our current research
work with efficient association rule mining approach in
secure manner over horizontal databases with our losing
the data integrity ,a secure session key can be generated
through efficient and improved Shamir secret share
technique and cipher data can be received and decrypted
by centralized server and finds the frequent patterns from
its end in an accurate and efficient manner
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BIOGRAPHIES
Dr. C.P.V.N.J Mohan Raois Professor in
the Department of Computer Science and
Engineering,
Avanthi Institute of
Engineering & Technology - Narsipatnam.
He did his PhD from Andhra University and
his research interests include Image Processing, Networks,
Information security, Data Mining and Software
Engineering. He has guided more than 50 M.Tech Projects
and currently guiding four research scholars for Ph.D. He
received many honors and he has been the member for
many expert committees, member of many professional
bodies and Resource person for various organizations.
SairamVakkalanka present working as
assistant professor in
department of
computer science and engineering at
Avanthi Institute of Engineering &
Technology – Narsipatnam and his research
interests are data mining and network
security
B.Ajit completed his BTech and currently
pursuing M.Tech in Department of
Computer Science and Engineering,
Avanthi Institute of Engineering &
Technology – Narsipatnam and his research
interests are data mining and network
security
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