Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/17677
Title: Development of transformation based privacy preservation methods for data mining
Researcher: Poovammal, E
Guide(s): Ponnavaikko, M
Keywords: Computer Science
privacy preservation methods
data mining
Upload Date: 11-Apr-2014
University: SRM University
Completed Date: October, 2010
Abstract: Data-mining is a task of discovering significant/salient patterns/rules/results from a set of large amount of data stored in databases, data warehouses or in other information repositories. Eventhough the focus on data-mining technology has been on the discovery of general patterns (not on any specific information regarding individuals) some data-mining applications may require access to individual s records having sensitive privacy data. Data containing structured information on individuals is referred to as micro-data. Abundance of recorded, personal information available in electronic form coupled with increasingly powerful data-mining tools, poses a threat to privacy and data security. The prime objective of this research is to find a solution to this problem. Eventhough, the identifying attributes are not published, some set of attributes in a released table (called quasi identifiers) may be linked with external data base leaking the sensitive data. To alleviate this problem, the so called K-anonymity and L-diversity principles and their improved versions have been used popularly in the earlier research works. But, all such methods suffer from proximity and divergence breach considerations. Also, the choice of generalization and diversity principles depends on the needs of underlying application, that is, application specific. Further, in such methods, if the level of privacy level is increased, information-loss also increases. And, these methods are successful only when the quasi-identifiers are explicitly identified with a surety of cent percent. Also, a set of organizations need to share their data so that datamining can be applied on the integrated data so as to get better rules/patterns/results. Normally, participating parties would like to accomplish mining tasks without disclosing sensitive information (in their databases) to other parties or to any third party. Various cryptographic techniques are used to preserve privacy of such collaborative data.
Pagination: 113p.
URI: http://hdl.handle.net/10603/17677
Appears in Departments:Department of Computer Science Engineering

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01_title.pdfAttached File53.76 kBAdobe PDFView/Open
02_declaration.pdf34.08 kBAdobe PDFView/Open
03_certificate.pdf36.4 kBAdobe PDFView/Open
04_acknowledgements.pdf41.39 kBAdobe PDFView/Open
05_abstract.pdf51.34 kBAdobe PDFView/Open
06_list of figures.pdf40.1 kBAdobe PDFView/Open
07_list of tables.pdf38.9 kBAdobe PDFView/Open
08_list of abbreviations.pdf35.62 kBAdobe PDFView/Open
09_contents.pdf42.65 kBAdobe PDFView/Open
10_chapter 1.pdf144.08 kBAdobe PDFView/Open
11_chapter 2.pdf49.41 kBAdobe PDFView/Open
12_chapter 3.pdf116.94 kBAdobe PDFView/Open
13_chapter 4.pdf114.87 kBAdobe PDFView/Open
14_chapter 5.pdf77.4 kBAdobe PDFView/Open
15_chapter 7.pdf48.27 kBAdobe PDFView/Open
16_chapter 8.pdf107.11 kBAdobe PDFView/Open
17_chapter 9.pdf53.88 kBAdobe PDFView/Open
18_references.pdf58.08 kBAdobe PDFView/Open
19_list of publications.pdf31.83 kBAdobe PDFView/Open
20_vitae.pdf25.66 kBAdobe PDFView/Open


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