PINQuin, a framework for differentially private analysis

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dc.contributor.authorTavallaei Ebadi, Hamid
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T13:08:38Z
dc.date.available2019-07-03T13:08:38Z
dc.date.issued2013
dc.description.abstractPrivacy is a humans right to seclude themselves, or information about themselves, from surveillance and view of others. Different cultures define and respect privacy differently but they usually share some basic concepts. Usually a special information that distincts one person or a group of population from others is considered personally sensitive. One aspect of privacy is related to anonymity which tries to remove or hide this personally sensitive information. Differential privacy is a robust standard that aims to protect individual's privacy when disclosing results from statistical analysis. In this master thesis we present a new model for differentially private data mining. As a result of this thesis we introduce a new method for privacy budgeting, namely record based differential privacy budgeting and PINQuin, our framework based on PINQ, that uses this new method. We also present experimental results comparing PINQuin with PINQ.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/176660
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titlePINQuin, a framework for differentially private analysis
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer systems and networks (MPCSN), MSc
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