PINQuin, a framework for differentially private analysis

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/176660
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Type: Examensarbete för masterexamen
Master Thesis
Title: PINQuin, a framework for differentially private analysis
Authors: Tavallaei Ebadi, Hamid
Abstract: Privacy 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.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2013
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/176660
Collection:Examensarbeten för masterexamen // Master Theses



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