Privacy Preserving Access of Outsourced Data in Heterogeneous Databases

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J.Bama
J.Bama
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Dr. M.S. Thanabal
Dr. M.S. Thanabal
α Anna University, Chennai Anna University, Chennai

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Privacy Preserving Access of Outsourced Data in Heterogeneous Databases

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Abstract

Privacy is main concern in the world, among present technological phase. Information security has become a dangerous issue since the information sharing has a common need. Recently, privacy issues have been increased enormously when internet is flourishing with forums, social media, blogs and e-commerce, etc. Hence research area is retaining privacy in data mining. The sensitive data of the data owners should not be known to the third parties and other data owners. To make it efficient, the horizontal partitioning is done on the heterogeneous databases is introduced to improve privacy and efficiency. we address the major issues of privacy preservation in information mining. In particular, we consider to provide protection between different data owners and to give privacy between them by partitioning the databases horizontally and the data’s are available in the heterogeneous databases. Our proposed work is to center around the study of security saving on unknown databases and conceiving private refresh methods to database frameworks that backings thoughts of obscurity assorted than k-secrecy.

References

9 Cites in Article
  1. Vanishree Arun,S Gowthami,S Padma (2015). Securely mining transactional databases for association rules using FDM.
  2. R Adhvaryu,N Domadiya (2014). Privacy Preserving in Association Rule Mining On Horizontally Partitioned Database.
  3. R Agrawal,R Srikant (1994). Fast algorithms for mining association rules.
  4. S Brossette,A Sprague,J Hardin,K Waites,W Jones,S Moser,B Mobasher,H Dai,T Luo,M Nakagawa (1998). Effective personalization based on association rule discovery from web usage data.
  5. Boris Rozenberg,Ehud Gudes (2006). Association rules mining in vertically partitioned databases.
  6. Mina Sheikhalishahi,Fabio Martinelli (2017). Privacy preserving clustering over horizontal and vertical partitioned data.
  7. X Yin,J Han (2003). CPAR: Classification based on predictive association rules.
  8. Justin Zhan,Stan Matwin,Liwu Chang (2005). Privacy-Preserving Collaborative Association Rule Mining.
  9. M Zaki (2000). Scalable algorithms for association mining.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

J.Bama. 2018. \u201cPrivacy Preserving Access of Outsourced Data in Heterogeneous Databases\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 18 (GJCST Volume 18 Issue C2): .

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Issue Cover
GJCST Volume 18 Issue C2
Pg. 15- 19
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-C Classification: H.2.5
Version of record

v1.2

Issue date

May 25, 2018

Language
en
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Privacy is main concern in the world, among present technological phase. Information security has become a dangerous issue since the information sharing has a common need. Recently, privacy issues have been increased enormously when internet is flourishing with forums, social media, blogs and e-commerce, etc. Hence research area is retaining privacy in data mining. The sensitive data of the data owners should not be known to the third parties and other data owners. To make it efficient, the horizontal partitioning is done on the heterogeneous databases is introduced to improve privacy and efficiency. we address the major issues of privacy preservation in information mining. In particular, we consider to provide protection between different data owners and to give privacy between them by partitioning the databases horizontally and the data’s are available in the heterogeneous databases. Our proposed work is to center around the study of security saving on unknown databases and conceiving private refresh methods to database frameworks that backings thoughts of obscurity assorted than k-secrecy.

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Privacy Preserving Access of Outsourced Data in Heterogeneous Databases

J.Bama
J.Bama Anna University, Chennai
Dr. M.S. Thanabal
Dr. M.S. Thanabal

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