Privacy Preserving Access of Outsourced Data in Heterogeneous Databases

J.Bama; Dr. M.S. Thanabal

Privacy Preserving Access of Outsourced Data in Heterogeneous Databases

J.Bama * §

Contact

Dr. M.S. Thanabal §

Contact

§ PSNA College of Engineering and Technology,

Privacy Preserving Access of Outsourced Data in Heterogeneous Databases

Article Fingerprint

ReserarchID

CSTSDE8YE9B

Privacy Preserving Access of Outsourced Data in Heterogeneous Databases Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant

This converted HTML version may contain rendering inconsistencies. Please refer to the PDF for the authoritative version, or click here to provide feedback.

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

Reference Format

1. Vanishree Arun,S Gowthami,S Padma (2015). Securely mining transactional databases for association rules using FDM. 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), 340-345.
2. R Adhvaryu,N Domadiya (2014). Privacy Preserving in Association Rule Mining On Horizontally Partitioned Database. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 3(5)
3. R Agrawal,R Srikant (1994). Fast algorithms for mining association rules. Proc. 20th int. conf. very large data bases, 1215, 487-499.
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. Proceedings of the 3rd international workshop on Web information and data management, 9-15.
5. Boris Rozenberg,Ehud Gudes (2006). Association rules mining in vertically partitioned databases. Data & Knowledge Engineering, 59(2), 378-396.
6. Mina Sheikhalishahi,Fabio Martinelli (2017). Privacy preserving clustering over horizontal and vertical partitioned data. 2017 IEEE Symposium on Computers and Communications (ISCC), 1237-1244.
7. X Yin,J Han (2003). CPAR: Classification based on predictive association rules. Proceedings of the 2003 SIAM International Conference on Data Mining, 331-335.
8. Justin Zhan,Stan Matwin,Liwu Chang (2005). Privacy-Preserving Collaborative Association Rule Mining. Lecture Notes in Computer Science, 153-165.
9. M Zaki (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372-390.

Download References

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, Dr. M.S. Thanabal. 2018. "Privacy Preserving Access of Outsourced Data in Heterogeneous Databases". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 18 (GJCST Volume 18 Issue C2).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 18 Issue C2

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-C Classification H.2.5

Submission ReceivedDecember 12, 2017
Peer Review Double Blind
Handling Editor
Accepted December 31, 2017
Published May 25, 2018

Version of record

v1.2

Issue date

May 25, 2018

Language

English

Order Article Reprint

Experience in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: J.Bama, Dr. M.S. Thanabal (PhD/Dr. count: 1)

View Count (all-time): 419

Total Views (Real + Logic): 1667

Total Downloads (simulated): 150

Publish Date: 2018 01, Mon

Monthly Totals (Real + Logic):

Month 1: 51 views
Month 2: 47 views
Month 3: 62 views
Month 4: 48 views
Month 5: 41 views
Month 6: 38 views
Month 7: 29 views
Month 8: 50 views
Month 9: 30 views
Month 10: 60 views
Month 11: 43 views
Month 12: 35 views
Month 13: 37 views
Month 14: 21 views
Month 15: 11 views
Month 16: 32 views
Month 17: 20 views
Month 18: 19 views
Month 19: 20 views
Month 20: 45 views
Month 21: 40 views
Month 22: 23 views
Month 23: 23 views
Month 24: 41 views
Month 25: 31 views
Month 26: 40 views
Month 27: 19 views
Month 28: 16 views
Month 29: 29 views
Month 30: 40 views
Month 31: 37 views
Month 32: 22 views
Month 33: 20 views
Month 34: 37 views
Month 35: 23 views
Month 36: 50 views
Month 37: 30 views
Month 38: 35 views
Month 39: 19 views
Month 40: 37 views
Month 41: 29 views
Month 42: 28 views
Month 43: 27 views
Month 44: 39 views
Month 45: 46 views
Month 46: 39 views
Month 47: 26 views
Month 48: 20 views
Month 49: 37 views
Month 50: 28 views
Month 51: 39 views
Month 52: 23 views
Month 53: 11 views
Month 54: 33 views
Month 55: 29 views
Month 56: 44 views
Month 57: 14 views
Month 58: 36 views
Month 59: 42 views
Month 60: 34 views
Month 61: 31 views
Month 62: 25 views
Month 63: 37 views
Month 64: 25 views
Month 65: 22 views
Month 66: 35 views
Month 67: 30 views
Month 68: 26 views
Month 69: 42 views
Month 70: 23 views
Month 71: 26 views
Month 72: 36 views
Month 73: 17 views
Month 74: 45 views
Month 75: 40 views
Month 76: 25 views
Month 77: 30 views
Month 78: 34 views
Month 79: 25 views
Month 80: 31 views
Month 81: 26 views
Month 82: 33 views
Month 83: 34 views
Month 84: 45 views
Month 85: 35 views
Month 86: 18 views
Month 87: 29 views
Month 88: 30 views
Month 89: 49 views
Month 90: 32 views
Month 91: 47 views
Month 92: 23 views
Month 93: 55 views
Month 94: 55 views
Month 95: 42 views
Month 96: 35 views
Month 97: 44 views
Month 98: 31 views
Month 99: 26 views
Month 100: 30 views
Month 101: 35 views
Month 102: 40 views
Month 103: 39 views
Month 104: 37 views
Month 105: 58 views
Month 106: 48 views

Total Views: 1.7K

Total Downloads: 150

All Trends