Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/262115
Title: Study on preserving privacy and minimizing information loss in data publishing
Researcher: Thirukumar K
Guide(s): Rathinavelu A
Keywords: Data Publishing
Engineering and Technology,Computer Science,Computer Science Information Systems
Preserving Privacy
University: Anna University
Completed Date: 2018
Abstract: The development of information technology and bulk electronic information owned by governments, corporations and individuals have resulted in greater need for data sharing. Huge amount of information sharing between people all over the world is increasing due to digital technology. Several organizations frequently combine with other entities for performing data distribution. In Healthcare domain, there is an emerging need for distribution of data which is constrained by the presence of personal information in database. The information shared between health organizations such as hospitals supports the use of Health information for enhancing the privacy. Privacy Preserving Data Publishing is the method to ensure that the presented data remains useful and protected. However, the PPDP methods remove the recognizing attributes like name and id but other attributes like gender, age, zipcode are collaborated to identify the individual and the confidential information such as disease and salary. Several methods are used to provide privacy for maintaining the database and reduce the attacks during data distribution. Privacy preservation can be used to identify abnormal behaviour in data sharing. Many privacy-preserving data publishing techniques were developed but failed to consider the medical dataset because of its complexity and domain specific nature. Various organizations release information about persons in public for resource sharing. But maintaining the confidentiality of individual information is a difficult task with various data releases from multiple organizations where coordinating before data publication. Several research works namely k-anonymity, l-diversity model were developed but avoidance of the above said composition attack is a challenging issue during data publication. newline
Pagination: xx, 133p.
URI: http://hdl.handle.net/10603/262115
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf3.88 MBAdobe PDFView/Open
03_abstract.pdf141.28 kBAdobe PDFView/Open
04_acknowledgement.pdf135.6 kBAdobe PDFView/Open
05_contents.pdf4.66 MBAdobe PDFView/Open
06_list_of_symbols_and_abbreviations.pdf109.83 kBAdobe PDFView/Open
07_chapter1.pdf1.05 MBAdobe PDFView/Open
08_chapter2.pdf245.59 kBAdobe PDFView/Open
09_chapter3.pdf1.09 MBAdobe PDFView/Open
10_chapter4.pdf1.07 MBAdobe PDFView/Open
11_chapter5.pdf276.33 kBAdobe PDFView/Open
12_chapter6.pdf110.96 kBAdobe PDFView/Open
13_references.pdf163.98 kBAdobe PDFView/Open
14_publications.pdf141.08 kBAdobe PDFView/Open


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