Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/258572
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dc.coverage.spatialInvestigation of Privacy Preserving Data Publication using Attribute Based Clustering and Encryption Techniques
dc.date.accessioned2019-09-18T12:35:37Z-
dc.date.available2019-09-18T12:35:37Z-
dc.identifier.urihttp://hdl.handle.net/10603/258572-
dc.description.abstractPrivacy preservation plays an important role in protecting sensitive data when sharing the information to public users in data mining. Privacy Preserving Data Publishing (PPDP) anonymizes the data through preserving the identity of individuals and sensitive information. Dimensionality reduction is a method of providing the high privacy rate for individual data within the database. Many research works have been done for preserving the data privacy on high dimensional data. However, nonymization technique reduces the privacy level and data utility due to the presence of various attacks while preserving the sensitive information for newlinedata distribution. Therefore, this research work focused on developing the privacy preservation through anonymization with reduced time complexity and higher anonymity level for efficient data publishing on high dimensional database. newlineAn accuracy constrained privacy preserving access control framework was planned for increasing the privacy level and access control based on the k-anonymous Partitioning with Imprecision Bounds (k-PIB). However, time complexity was minimized. Slicing technique was designed for preserving the attribute disclosure and also evaluates the sliced data based on l-diversity requirement. Though, the data utility was not sufficient due to random generation of connections between column values of bucket on high dimensional data. In order to improve the data utility, an anonymization technique was developed based on Nearest-Neighbor (NN) search and global data reorganization on sparse high-dimensional data. This in turn reduces the information loss with high privacy. newline newline
dc.format.extentxxi, 174p.
dc.languageEnglish
dc.relationp.167-173
dc.rightsuniversity
dc.titleInvestigation of privacy preserving data publication using attribute based clustering and encryption techniques
dc.title.alternative
dc.creator.researcherVanathi D
dc.subject.keywordClustering
dc.subject.keywordData Publication
dc.subject.keywordEncryption Techniques
dc.subject.keywordEngineering and Technology,Computer Science,Computer Science Interdisciplinary Applications
dc.description.note
dc.contributor.guideSengottuvelan P
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registeredn.d.
dc.date.completed2018
dc.date.awarded31/12/2018
dc.format.dimensions21 cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File17.97 kBAdobe PDFView/Open
02_certificates.pdf6.15 MBAdobe PDFView/Open
03_abstract.pdf116.02 kBAdobe PDFView/Open
04_acknowledgement.pdf106.04 kBAdobe PDFView/Open
05_table_of_contents.pdf129.79 kBAdobe PDFView/Open
06_list_of_symbols_and_abbreviations.pdf968.74 kBAdobe PDFView/Open
07_chapter1.pdf1.01 MBAdobe PDFView/Open
08_chapter2.pdf989.11 kBAdobe PDFView/Open
09_chapter3.pdf1.05 MBAdobe PDFView/Open
10_chapter4.pdf1.25 MBAdobe PDFView/Open
11_chapter5.pdf1.18 MBAdobe PDFView/Open
12_chapter6.pdf1.12 MBAdobe PDFView/Open
13_conclusion.pdf95.05 kBAdobe PDFView/Open
14_references.pdf157.61 kBAdobe PDFView/Open
15_list_of_publications.pdf149.78 kBAdobe PDFView/Open


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