Open AccessPrivacy Preserving with Association Rule Mining using Evolutionary Algorithm
Author(s) -
Sasanko Sekhar Gantayat,
Bichitrananda Patra,
Niranjan Panda,
Manoranjan Parhi
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d9701.118419
Subject(s) - association rule learning , computer science , information sensitivity , secrecy , data mining , association (psychology) , raw data , sensitivity (control systems) , algorithm , database , information retrieval , computer security , engineering , philosophy , epistemology , electronic engineering , programming language
Privacy-Preserving-Data-Mining (PPDM) is a novel study which goals to protect the secretive evidence also circumvent the revelation of the evidence through the records reproducing progression. This paper focused on the privacy preserving on vertical separated databases. The designed methodology for the subcontracted databases allows multiple data viewers besides vendors proficiently to their records securely without conceding the secrecy of the data. Privacy Preserving Association Rule-Mining (PPARM) is one method, which objects to pelt sensitivity of the association imperative. A new efficient approach lives the benefit since the strange optimizations algorithms for the delicate association rule hiding. It is required to get leak less information of the raw data. The evaluation of the efficient of the proposed method can be conducting on some experiments on different databases. Based on the above optimization algorithm, the modified algorithm is to optimize the association rules on vertically and horizontally separated database and studied their performance