Title:
A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering

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dc.contributor.advisor Blough, Douglas M.
dc.contributor.author Parameswaran, Rupa en_US
dc.contributor.committeeMember Fekri, Faramarz
dc.contributor.committeeMember Navathe, Sham
dc.contributor.committeeMember Schimmel, David
dc.contributor.committeeMember Wills, Linda
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2006-09-01T19:05:07Z
dc.date.available 2006-09-01T19:05:07Z
dc.date.issued 2006-05-10 en_US
dc.description.abstract Privacy is defined as the freedom from unauthorized intrusion. The availability of personal information through online databases, such as government records, medical records, and voters and #146; lists, pose a threat to personal privacy. The concern over individual privacy has led to the development of legal codes for safeguarding privacy in several countries. However, the ignorance of individuals as well as loopholes in the systems, have led to information breaches even in the presence of such rules and regulations. Protection against data privacy requires modification of the data itself. The term {em data obfuscation} is used to refer to the class of algorithms that modify the values of the data items without distorting the usefulness of the data. The main goal of this thesis is the development of a data obfuscation technique that provides robust privacy protection with minimal loss in usability of the data. Although medical and financial services are two of the major areas where information privacy is a concern, privacy breaches are not restricted to these domains. One of the areas where the concern over data privacy is of growing interest is collaborative filtering. Collaborative filtering systems are being widely used in E-commerce applications to provide recommendations to users regarding products that might be of interest to them. The prediction accuracy of these systems is dependent on the size and accuracy of the data provided by users. However, the lack of sufficient guidelines governing the use and distribution of user data raises concerns over individual privacy. Users often provide the minimal information that is required for accessing these E-commerce services. The lack of rules governing the use and distribution of data disallows sharing of data among different communities for collaborative filtering. The goals of this thesis are (a) the definition of a standard for classifying DO techniques, (b) the development of a robust cluster preserving data obfuscation algorithm, and (c) the design and implementation of a privacy-preserving shared collaborative filtering framework using the data obfuscation algorithm. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 3739541 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/11459
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Data privacy en_US
dc.subject Data obfuscation
dc.subject Data mining
dc.subject Collaborative filtering
dc.subject.lcsh Data mining en_US
dc.subject.lcsh Information storage and retrieval systems en_US
dc.subject.lcsh Cluster analysis Computer programs en_US
dc.subject.lcsh Database security en_US
dc.title A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Blough, Douglas M.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 361410e1-2656-48cf-8d91-a4cd3d538c29
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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