Defending the Sensitive Data using Lattice Structure in Privacy Preserving Association Rule Mining
Innovation of association rules from enormous databases ensures bene#64257;ts for the enterprises
since such rules can be very operative in enlightening the knowledge that leads to tactical decisions. Association
rule mining has acknowledged a proportion of attention in the collaborative business community
and several algorithms were proposed to improve the performance of association rules or frequent itemset
mining. The man-made data generators have been generally used for performance estimation. Latest
works shows that the data generated is not worthy su#64259;cient for standardizing as it has very dissimilar
characteristics from real-world data sets. Hence forth there is an abundant need to use real-world data
sets as standard. But, organizations hesitate to provide their data due to privacy concerns.Privacy preserving
association rule mining addresses this problem by transforming the real data sets to hide sensitive
or secretive rules. Though, transforming sensitive data in real data may in#64258;uence other non-sensitive
rules. One essential feature of privacy preserving association rule mining is the fact that the mining process
deals with a trade-o#64256; between privacy and accuracy, which are typically con#64258;icting, and improving
one typically incurs a cost in the other. In this paper, we present a novel algorithm for balancing privacy
and knowledge discovery in association rule mining. We use the concepts of sensitivity of the transaction
and itemset lattice, to identify the transactions that are to be transformed and the item that is to be
transformed respectively.The algorithm is experimentally assessed with a real data set and a synthetic
data set. The analysis illustrate that our methodology is e#64256;ective and e#64259;cient for restructuring real
world data sets for a given set of sensitive association rules while preserving non-sensitive association
rules.
Keywords: Lattice Structure, Privacy Preserving, Accuracy, Sensitive Data, Impact-Factor
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ABOUT THE AUTHORS
B.Janakiramaiah
He was born in 1979. He received his bachelor’s degree in Computer Science and Engineering from Nagpur University, Masters in Computer Science and Engineering from Jawaharlal Nehru Technological University. He is currently working as Associate Professor in DVR & Dr HS MIC College of Technology, Kanchikacherla, India. He is now research scholar in JNTUH, Hyderabad, India. His interests are Privacy preserving data mining, Machine Learning, Soft Computing.
A.Ramamohan Reddy
He was born in 1958. He received his Masters in Computer Science and Engineering from NIT, Warangal, Ph.D in Computer Science and Engineering from Sri Venkateswara University, Tirupati. He is currently working as a Professor and Head of Computer Science and Engineering department in SVU College of Engineering, Tirupati, India. His interests are Data Mining, Software Engineering and Software Architectures.
G.Kalyani
She was born in 1979. She received her bachelor’s degree in Computer Science and Engineering from Acharya Nagarjuna University, Masters in Computer Science and Engineering from Jawaharlal Nehru Technological University. She is currently working as Associate Professor in DVR & Dr HS MIC College of Technology, Kanchikacherla, India. Her interests are Privacy preserving data mining, Machine Learning, Operating Systems, Data Base Management Systems.
B.Janakiramaiah
He was born in 1979. He received his bachelor’s degree in Computer Science and Engineering from Nagpur University, Masters in Computer Science and Engineering from Jawaharlal Nehru Technological University. He is currently working as Associate Professor in DVR & Dr HS MIC College of Technology, Kanchikacherla, India. He is now research scholar in JNTUH, Hyderabad, India. His interests are Privacy preserving data mining, Machine Learning, Soft Computing.
A.Ramamohan Reddy
He was born in 1958. He received his Masters in Computer Science and Engineering from NIT, Warangal, Ph.D in Computer Science and Engineering from Sri Venkateswara University, Tirupati. He is currently working as a Professor and Head of Computer Science and Engineering department in SVU College of Engineering, Tirupati, India. His interests are Data Mining, Software Engineering and Software Architectures.
G.Kalyani
She was born in 1979. She received her bachelor’s degree in Computer Science and Engineering from Acharya Nagarjuna University, Masters in Computer Science and Engineering from Jawaharlal Nehru Technological University. She is currently working as Associate Professor in DVR & Dr HS MIC College of Technology, Kanchikacherla, India. Her interests are Privacy preserving data mining, Machine Learning, Operating Systems, Data Base Management Systems.
