A Comprehensive Review and Efficient Implementation of Privacy-Preserving Associative Classification

Volume 7 Issue 3 June - August 2018

Review Paper

A Comprehensive Review and Efficient Implementation of Privacy-Preserving Associative Classification

Darshana H. Patel*, Avani R. Vasant**, Saurabh Shah***

* Head and Assistant Professor, Department of Information Technology, V. V. P. Engineering College, Gujarat, India.

** Professor and Head, Department of Computer Science and Engineering, Babaria Institute of Technology, Gujarat, India.

*** Director and Professor, Department of Computer Engineering, C.U. Shah University, Wadhwan, Gujarat, India.

Patel,D.H., Avsant,A.R., and Shah,S.(2018). A Comprehensive Review and Efficient Implementation of Privacy-Preserving Associative Classification. i-manager’s Journal on Information Technology, 7(3), 45-52. https://doi.org/10.26634/jit.7.3.14406

Abstract

The enormous development in Information and Communications technology had increased the requirement for digital data to be stock up and communal securely. This immense quantity of data, if publicly available, can be employed for growth and development. However, data in its raw form comprises of sensitive information and advances in data mining techniques have increased the breach of privacy or confidential data. As a consequence, a field of privacy-preserving data mining emerged which deals with efficient conduction and application of data mining functionalities without scarifying the privacy of the data. Nevertheless, data should be mined before preserving privacy and amongst various techniques of data mining, associative classification is favorable for classification purpose. This paper focuses on the study of Associative Classification techniques in addition to privacy preserving techniques along with its pros and cons. In addition, related study of privacy preserving associative classification has been presented with an aim of prolific delve in this area. Furthermore, privacy preserving association classification has been implemented utilizing various datasets considering the accuracy parameter and it has been concluded that as privacy increases, accuracy gets degraded due to data transformation.