Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/396265
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dc.date.accessioned2022-07-29T05:08:22Z-
dc.date.available2022-07-29T05:08:22Z-
dc.identifier.urihttp://hdl.handle.net/10603/396265-
dc.description.abstractquotHumongous amount of data gets collected by various online applications like social networks, cellular technologies, the healthcare sector, location - based services, newlineand many more. The collected data can be accessed by third - party applications to study social and economic issues of society, leverage research, propose healthcare and business solutions, and even track a pandemic. As a result, online collected - data is a significant contributor in recent times. Despite the umpteen usefulness of online collected - data, it is vulnerable to privacy threats due to the presence of sensitive information of individual(s). Adding to that, the adversary has also become strong and powerful in terms of capabilities and access to knowledge. Knowledge is freely available in the public domain from sources like social profiles, social relations, previously published data and many more. As a result, privacy - preserving data publishing is a challenging research direction to venture upon. Our work mainly focuses on designing privacy models against background knowledge. Briefly, background knowledge is knowledge present with adversary used to disclose privacy of the individual(s). This makes background knowledge highly uncertain and inaccurate in nature as we cannot quantify the amount of knowledge present with the adversary. In this work, we design and analyze privacy solutions based on background knowledge. First of all, we propose an adversarial model against background knowledge and analyze existing and prominent newlineprivacy models against it. Secondly, we propose a privacy model (q, [lb, ub]+sp, a)- Private against background knowledge. The background knowledge assumption is comprehensive and realistic, which makes the proposed privacy model more strong and comprehensive in nature. The proposed privacy model has been theoretically analyzed against a strong adversary. Also, the proposed privacy model has been evaluated experimentally and compared with existing literature. Progressively, our research work extends to Social...
dc.format.extentxiii, 178 p.
dc.languageEnglish
dc.relationDeasai, Nidhi Nitin, Design and Analysis of Privacy Models against Background Knowledge in Privacy Preserving Data Publishing; xiii, 178 p.; 2021. (Supervisor: Manik Lal Das)
dc.rightsuniversity
dc.titleDesign and Analysis of Privacy Models against Background Knowledge in Privacy Preserving Data Publishing
dc.title.alternative
dc.creator.researcherDeasai, Nidhi Nitin
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Cybernetics
dc.subject.keywordPrivacy
dc.subject.keywordLegal literature--Publishing
dc.subject.keywordSocial networks--Computer network resources
dc.subject.keywordOnline social networks
dc.subject.keywordOperating leverage
dc.subject.keywordData collection platforms
dc.description.note
dc.contributor.guideDas, Manik Lal
dc.publisher.placeGandhinagar
dc.publisher.universityDhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)
dc.publisher.institutionDepartment of Information and Communication Technology
dc.date.registered2014
dc.date.completed2021
dc.date.awarded2021
dc.format.dimensions30 cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Information and Communication Technology

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01_title.pdfAttached File80.92 kBAdobe PDFView/Open
02_declaration and certificate.pdf74.28 kBAdobe PDFView/Open
03_acknowledgments.pdf56.14 kBAdobe PDFView/Open
04_contents.pdf93.99 kBAdobe PDFView/Open
05_abstract.pdf87.73 kBAdobe PDFView/Open
06_list of tables , figures, and acronyms.pdf119.08 kBAdobe PDFView/Open
07_chapter 1.pdf240.2 kBAdobe PDFView/Open
08_chapter 2.pdf218.54 kBAdobe PDFView/Open
09_chapter 3.pdf247.11 kBAdobe PDFView/Open
10_chapter 4.pdf581.55 kBAdobe PDFView/Open
11_chapter 5.pdf104.77 kBAdobe PDFView/Open
12_chapter 6.pdf284.78 kBAdobe PDFView/Open
13_chapter 7.pdf330 kBAdobe PDFView/Open
14_chapter 8.pdf91.46 kBAdobe PDFView/Open
15_references.pdf112.99 kBAdobe PDFView/Open
16_appendix.pdf105.94 kBAdobe PDFView/Open
80_recommendation.pdf126.03 kBAdobe PDFView/Open


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