Stable Privacy Parameter Settings Using Game Theory
| atmire.migration.oldid | 820 | |
| dc.contributor.advisor | Barker, Ken | |
| dc.contributor.author | Karimi Adl, Rosa | |
| dc.date.accessioned | 2013-04-16T21:35:33Z | |
| dc.date.available | 2015-04-17T07:00:28Z | |
| dc.date.issued | 2013-04-16 | |
| dc.date.submitted | 2013 | en |
| dc.description.abstract | Privacy protection appears as a fundamental concern when personal data is collected, stored, and published. Several privacy protection methods have been proposed to address privacy issues in private datasets. Each method has at least one parameter to adjust the guaranteed level of privacy protection. As the privacy protection level increases, the dataset loses more information utility due to further application of data manipulation methods and/or access restriction rules. Consequently, balancing the trade ff between privacy and utility is a crucial step and so far no systematic mechanism exists to provide directions on how to establish values for privacy parameters such that a balanced privacy/utility tradeff is induced. A balanced privacy/utility tradeoff can be described as a level on which the stakeholders of data reach a consensus (in the sense that no single party would be wiling to act diff erently to change the agreed upon level). Game theory provides a natural solution to finding such balanced tradeoff s. In this thesis, we capture the essence of establishing balancing values for privacy parameters as an extensive-form game with incomplete and imperfect information. A high-level step-by-step guideline is provided on how to solve the generic game. We instantiate the generic game model for three different privacy protection methods and analytically solve each game. The games' solutions are further simulated for sample problem settings to study the effects of various problem parameters on the balancing values of privacy parameters. The game model and its solution contribute to the fulfillment of our objective of establishing balancing values for privacy parameters (of a chosen privacy protection method). In addition to our main objective, the proposed game model can be consulted to choose the most pro fitable privacy protection method based on the problem requirements. Benchmarking frameworks can also benefi t from our game solutions by using the balancing privacy parameter values as the reference points for the comparisons between different privacy protection methods. We believe that a first step towards improving the data collection and privacy protection procedures is to understand how much privacy is currently sacrificed to achieve information utility (at the steady states). The game-based solution provided in this thesis promotes a deeper understanding of how privacy and utility reach a balanced tradeoff within the current privacy protection methods. | en_US |
| dc.description.embargoterms | 2 years | en_US |
| dc.identifier.citation | Karimi Adl, R. (2013). Stable Privacy Parameter Settings Using Game Theory (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27868 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/27868 | |
| dc.identifier.uri | http://hdl.handle.net/11023/603 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Computer Science | |
| dc.subject.classification | Privacy | en_US |
| dc.subject.classification | Game Theory | en_US |
| dc.title | Stable Privacy Parameter Settings Using Game Theory | |
| dc.type | doctoral thesis | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Doctor of Philosophy (PhD) | |
| ucalgary.item.requestcopy | true |