Field Level Security of the Sensitive Data in Large Datasets
K. Shirisha1, K. Haritha2
1K.Shirisha*, Professor, Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana State, India.
2Haritha Kunta, Associate Success Agent, Salesforce.com India Pvt. Ltd. Hyderabad, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 237-240 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1867029420/2020©BEIESP | DOI: 10.35940/ijitee.D1867.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The process of deriving useful and knowledgeable information from enormous quantity of data is Data Mining. During mining procedures, handling of the sensitive data has become important to protect data against illegal attacks and malicious access either during transmission or at rest. Association rule algorithm is one of the rule extraction techniques. The rules determined are either to be transferred over the public networks or to be rested for further use. The main objective of the Field Level Security of the Sensitive Data in Large Datasets is to extract the strong association rules from the large data sets and the outcomes are crafted to conceal the sensitive data. The datasets and the association rules involving the attributes with relationships and dependencies are modified through several approaches and to see that no sensitive association rule is derived from it[1]. Privacy preservation of the sensitive association rules in large datasets is to provide secrecy for the sensitive data. Presently, it has become quite important to safeguard the privacy of the users’ personal data from unauthorized persons. The usage of association rules in voluminous datasets has emerged to be advantageous to organizations [2]. In this paper, we present a novel approach which is applied for hiding sensitive association rules by utilizing the techniques of compression, encryption method ology on the original dataset, providing dataset with better immunity.
Keywords: Association Rules, Data Mining, Privacy Preservation, Sensitive Data
Scope of the Article: Data Mining