Adaptable Privacy-preserving Model

Author

Emily Elizabeth Brown, Nova Southeastern UniversityFollow

Date of Award

2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

College of Engineering and Computing

Advisor

James L. Cannady

Committee Member

James L. Parrish

Committee Member

Steven R. Terrell

Keywords

anonymization models, data privacy, data processing, k-anonymity, l-diversity, privacy-preserving models

Abstract

Current data privacy-preservation models lack the ability to aid data decision makers in processing datasets for publication. The proposed algorithm allows data processors to simply provide a dataset and state their criteria to recommend an xk-anonymity approach. Additionally, the algorithm can be tailored to a preference and gives the precision range and maximum data loss associated with the recommended approach. This dissertation report outlined the research’s goal, what barriers were overcome, and the limitations of the work’s scope. It highlighted the results from each experiment conducted and how it influenced the creation of the end adaptable algorithm. The xk-anonymity model built upon two foundational privacy models, the k-anonymity and l-diversity models. Overall, this study had many takeaways on data and its power in a dataset.

NSUWorks Citation

Emily Elizabeth Brown. 2019. Adaptable Privacy-preserving Model. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. (1069)
https://nsuworks.nova.edu/gscis_etd/1069.