IJSART - Volume 4 Issue 2 – FEBRUARY 2018 ISSN [ONLINE]: 2395-1052 Page | 637 www.ijsart.com A Case Study on Issues In Privacy Preserving Data Mining Siripuri Kiran 1 , Ajmera Rajesh 2 Assistant Professor 1 , Academic Consultant 2 1, 2 Kakatiya Institute Of Technology and Sciences, Warangal, India. 2 Ku College Of Engineering And Technology, Kakatiya University, Warangal. India. Abstract- The development in data mining technology brings serious threat to the individual information. The objective of privacy preserving data mining (PPDM) is to safeguard the sensitive information contained in the data. The unwanted disclosure of the sensitive information may happen during the process of data mining results. In this study we identify four different types of users involved in mining application i.e. data source provider, data receiver, data explorer and determiner decision maker. We would like to provide useful insights into the study of privacy preserving data mining. This paper presents a comprehensive noise addition technique for protecting individual privacy in a data set used for classification, while maintaining the data quality. We add noise to all attributes, both numerical and categorical, and both to class and non-class, in such a way so that the original patterns are preserved in a perturbed data set. Our technique is also capable of incorporating previously proposed noise addition techniques that maintain the statistical parameters of the data set, including correlations among attributes. Thus the perturbed data set may be used not only for classification but also for statistical analysis. Keywords- Data Mining, Security, Issues & Remedies, Privacy, Preservation, development, technology, information, process. I. INTRODUCTION Data mining is frequently characterized as the way toward finding important, new correlation patterns and trends through non-trifling extraction of certain, already obscure data from extensive measure of data put away in repositories utilizing design acknowledgment and additionally statistical and mathematical techniques. A Structured Query Language (SQL) is usually stated or written to access a specific data while data miners might not even be exactly sure of what they need. So, the result of a SQL query is usually a part of the database; whereas the result of a data mining query is an analysis of full contents of the database. Data mining tasks can be classified as follows: 1) Association rule mining or market basket analysis 2) Classification and prediction 3) Cluster analysis and outlier analysis 4) Web Data mining and search engines’. 5) Evolution analysis The main focus of this thesis is to obtain secure Clustering results. Achieving accurate clustering results by providing privacy to sensitive data is trivial task. This thesis proposes two approaches for achieving the privacy for sensitive attributes during data mining [1]. Data Mining Data mining also called as knowledge discovery in databases (KDD). Data Mining is defined as the “process of evaluating interesting, useful and hidden patterns from large volumes of data stores and identifies the relationships among the patterns” [2-4]. Data mining task requires utilities fir statistical data and Artificial Intelligence systems (AI). AI systems includes neural networks and machine learning sometimes one can combine them with database management system for evaluating or analyzing the huge volumes of digital data, which is the derived form of data sets.. Data mining has many applications; those have been listed in the above section. They can broadly categorized in to three area’s one is business (insurance company, banking corporation, retail sector), second is science research (astronomy, medicine), and government security (detection of criminals and terrorists). The large number of organizations, government and private data bases aims to ensure that the individual records are accurate and secure from unauthorized access. The tasks of data mining are targeted towards extracting hidden predictive knowledge about a group rather than the individual. Figure 1 shows the Data mining process. First, data is collected from various sources in Data selection step. Next, Data will be pre- processed by dealing with null values and unformatted values. Then, Data will be transformed to proper format which is suitable for data mining operation [5]. Now,