Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases

Authors(3) :-K Geetha, K Gurunadha Guptha, S N V A S R K Prasad

Data Analysis techniques that are Association manage mining and Frequent thing set mining are two prominent and broadly utilized for different applications. The conventional framework concentrated independently on vertically parceled database and on a level plane apportioned databases on the premise of this presenting a framework which concentrate on both on a level plane and vertically divided databases cooperatively with protection safeguarding component. Information proprietors need to know the continuous thing sets or affiliation rules from an aggregate information set and unveil or uncover as few data about their crude information as could reasonably be expected to other information proprietors and outsiders. To guarantee information protection a Symmetric Encryption Technique is utilized to show signs of improvement result. Cloud supported successive thing set mining arrangement used to exhibit an affiliation govern mining arrangement. The subsequent arrangements are intended for outsourced databases that permit various information proprietors to proficiently share their information safely without trading off on information protection. Information security is one of the key procedures in outsourcing information to different outside clients. Customarily Fast Distribution Mining calculation was proposed for securing conveyed information. These business locales an issue by secure affiliation governs over parceled information in both even and vertical. A Frequent thing sets calculation and Distributed affiliation administer digging calculation is used for doing above method adequately in divided information, which incorporates administrations of the information in outsourcing process for disseminated databases. This work keeps up or keeps up proficient security over vertical and flat perspective of representation in secure mining applications.

Authors and Affiliations

K Geetha
Department of Computer Science and Engineering, Sri Indu College of Engineering & Technology, Telangana, India
K Gurunadha Guptha
Department of Computer Science and Engineering, Sri Indu College of Engineering & Technology, Telangana, India
S N V A S R K Prasad

Association Rules Mining, Frequent Item Set Mining, Privacy-Preserving Data Mining, Partitioned Data

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 280-286
Manuscript Number : CSEIT172483
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K Geetha, K Gurunadha Guptha, S N V A S R K Prasad, "Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.280-286, July-August-2017. |          | BibTeX | RIS | CSV

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