Data Leakage Detection by Using Fake Objects

Data Leakage Detection by Using Fake Objects

Rama Rajeswari Mulukutla

Contact

P.Poturaju

Contact

Jawaharlal Nehru Technological University, Kakinada

Data Leakage Detection by Using Fake Objects

Article Fingerprint

ReserarchID

CSTSDEE901A

Data Leakage Detection by Using Fake Objects Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

Modern business activities rely on extensive email exchange. Email leakage have became widespread throughout the world, and severe damage has been caused by these leakages it constitutes a problem for organization. We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties).If the data distributed to the third parties is found in a publicprivate domain then finding the guilty party is a nontrivial task to a distributor. Traditionally, this leakage of data has handled by water marking technique which requires modification of data. If the watermarked copy is found at Some unauthorized site then distributor claim his ownership. To overcome the disadvantage of using watermark, data allocation strategies are used to improve the probability of identifying guilty third parties. The distributor must assess the likelihood that the leaked data come from one or more agents, as opposed to having been gathered from other means. In this project, we implement and analyze a guilt model that detects the agents using allocation strategies without modifying the original data .the guilt agent is one who leaks a portion of distributed data. We propose data “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party. And Algorithms implemented using fake objects will improve the distributor chance of detecting the guilt agent. It is observed that by minimizing the sum objective the chance of detecting guilt agents will increase. We also develop a framework for generating fake objects.

References

8 Cites in Article

Reference Format

Rakesh Agrawal,Jerry Kiernan (2002). Watermarking Relational Databases.
Piero Bonatti,Sabrina De Capitani Di Vimercati,Pierangela Samarati (2002). An algebra for composing access control policies.
Peter Buneman,Wang-Chiew Tan (2007). Provenance in databases.
F Hartung,B Girod (1998). Watermarking of Uncompressed and Compressed Video.
B Mungamuru,H Garcia-Molina (2008). Privacy, Preservation and Performance: The 3 P's of Distributed Data Management.
P Papadimitriou,H Garcia-Molina (2008). Data Leakage Detection.
Latanya Sweeney (2002). ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION.
Y Li,V Swarup,S Jajodia (2005). Fingerprinting Relational Databases: Schemes and Specialties.

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

Rama Rajeswari Mulukutla. 2013. \u201cData Leakage Detection by Using Fake Objects\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C6).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 13 Issue C6

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Allocation strategies data leakage data privacy fake records leakage model

Submission ReceivedDecember 13, 2012
Peer Review Double Blind
Handling Editor
Accepted January 4, 2013
Published January 15, 2013

Version of record

v1.2

Issue date

June 4, 2013

Language

Experiance 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: 102

Country: India

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

Authors: Rama Rajeswari Mulukutla, P.Poturaju (PhD/Dr. count: 0)

View Count (all-time): 267

Total Views (Real + Logic): 9537

Total Downloads (simulated): 2527

Publish Date: 2013 06, Tue

Monthly Totals (Real + Logic):

Month 1: 48 views
Month 2: 43 views
Month 3: 44 views
Month 4: 46 views
Month 5: 23 views
Month 6: 30 views
Month 7: 33 views
Month 8: 27 views
Month 9: 25 views
Month 10: 38 views
Month 11: 21 views
Month 12: 19 views
Month 13: 21 views
Month 14: 35 views
Month 15: 19 views
Month 16: 19 views
Month 17: 29 views
Month 18: 27 views
Month 19: 29 views
Month 20: 35 views
Month 21: 49 views
Month 22: 16 views
Month 23: 29 views
Month 24: 30 views
Month 25: 32 views
Month 26: 34 views
Month 27: 30 views
Month 28: 22 views
Month 29: 42 views
Month 30: 49 views
Month 31: 20 views
Month 32: 35 views
Month 33: 22 views
Month 34: 14 views
Month 35: 21 views
Month 36: 47 views
Month 37: 46 views
Month 38: 26 views
Month 39: 32 views
Month 40: 27 views
Month 41: 22 views
Month 42: 37 views
Month 43: 50 views
Month 44: 27 views
Month 45: 25 views
Month 46: 23 views
Month 47: 21 views
Month 48: 46 views
Month 49: 33 views
Month 50: 41 views
Month 51: 43 views
Month 52: 47 views
Month 53: 22 views
Month 54: 23 views
Month 55: 22 views
Month 56: 21 views
Month 57: 33 views
Month 58: 26 views
Month 59: 27 views
Month 60: 28 views
Month 61: 46 views
Month 62: 27 views
Month 63: 44 views
Month 64: 27 views
Month 65: 27 views
Month 66: 31 views
Month 67: 22 views
Month 68: 19 views
Month 69: 20 views
Month 70: 37 views
Month 71: 22 views
Month 72: 41 views
Month 73: 31 views
Month 74: 39 views
Month 75: 38 views
Month 76: 28 views
Month 77: 28 views
Month 78: 38 views
Month 79: 34 views
Month 80: 30 views
Month 81: 34 views
Month 82: 41 views
Month 83: 21 views
Month 84: 24 views
Month 85: 41 views
Month 86: 41 views
Month 87: 40 views
Month 88: 42 views
Month 89: 27 views
Month 90: 44 views
Month 91: 20 views
Month 92: 36 views
Month 93: 29 views
Month 94: 40 views
Month 95: 23 views
Month 96: 31 views
Month 97: 40 views
Month 98: 26 views
Month 99: 17 views
Month 100: 32 views
Month 101: 27 views
Month 102: 13 views
Month 103: 37 views
Month 104: 35 views
Month 105: 25 views
Month 106: 45 views
Month 107: 34 views
Month 108: 23 views
Month 109: 18 views
Month 110: 38 views
Month 111: 22 views
Month 112: 41 views
Month 113: 42 views
Month 114: 30 views
Month 115: 45 views
Month 116: 32 views
Month 117: 14 views
Month 118: 24 views
Month 119: 35 views
Month 120: 19 views
Month 121: 34 views
Month 122: 30 views
Month 123: 46 views
Month 124: 37 views
Month 125: 29 views
Month 126: 25 views
Month 127: 37 views
Month 128: 15 views
Month 129: 41 views
Month 130: 20 views
Month 131: 32 views
Month 132: 24 views
Month 133: 28 views
Month 134: 25 views
Month 135: 24 views
Month 136: 31 views
Month 137: 45 views
Month 138: 24 views
Month 139: 41 views
Month 140: 29 views
Month 141: 11 views
Month 142: 30 views
Month 143: 27 views
Month 144: 28 views
Month 145: 28 views
Month 146: 22 views
Month 147: 30 views
Month 148: 42 views
Month 149: 46 views
Month 150: 40 views
Month 151: 15 views
Month 152: 24 views
Month 153: 37 views
Month 154: 44 views
Month 155: 36 views
Month 156: 39 views

Total Views: 9537

Total Downloads: 2527

2026 Trends

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]