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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

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Impact Factor : 8.14

Issue per Year : 12

Volume Published : 7

Issue Published : 75

Article Submitted : 4301

Article Published : 2432

Total Authors : 6372

Total Reviewer : 533

Total Countries : 46

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Paper Title: PRIVACY PRESERVING MINING ON PARTIALLY JOINT DATABASE INTO MINING AN ASSOCIATION RULE
Authors Name: C.Mahalakshmi , R.Muthamizharasi , S.R.Vinotha
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IJRTI_170023
Published Paper Id: IJRTI1703001
Published In: Volume 2 Issue 2, March-2017
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Abstract: Association rule mining and frequent item set mining are two popular and widely studied data analysis techniques for a range of applications. In this paper, we focus on privacy-preserving mining on vertically partitioned databases. In such a scenario, data owners wish to learn the association rules or frequent item sets from a collective data set and disclose as little information about their (sensitive) raw data as possible to other data owners and third parties. To ensure data privacy, we design an efficient homomorphism encryption scheme and a secure comparison scheme. We then propose a cloud-aided frequent item set mining solution, which is used to build an association rule mining solution. Our solutions are designed for outsourced databases that allow multiple data owners to efficiently share their data securely without compromising on data privacy. Our solutions leak less information about the raw data than most existing solutions. In comparison to the only known solution achieving a similar privacy level as our proposed Solutions, the performance of our proposed solutions is three to five orders of magnitude higher. Based on our experiment findings using different parameters and data sets, we demonstrate that the run time in each of our solutions is only one order higher than that in the best non-privacy-preserving data mining algorithms. Since both data and computing work are outsourced to the cloud servers, the resource consumption at the data owner end is very low.
Keywords: : Association rule mining, frequent item set mining, homomorphic encryption scheme
Cite Article: "PRIVACY PRESERVING MINING ON PARTIALLY JOINT DATABASE INTO MINING AN ASSOCIATION RULE", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.2, Issue 2, page no.1 - 6, March-2017, Available :http://www.ijrti.org/papers/IJRTI1703001.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI1703001
Registration ID:170023
Published In: Volume 2 Issue 2, March-2017
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Page No: 1 - 6
Country: trichy, Tamil Nadu, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI1703001
Published Paper PDF: https://www.ijrti.org/papers/IJRTI1703001
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

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