Publication Date
5-2018
Date of Final Oral Examination (Defense)
3-12-2018
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Mathematics
Department
Mathematics
Major Advisor
Gaby Dagher, Ph.D.
Advisor
Lijana Babinkostova, Ph.D.
Advisor
Marion Scheepers, Ph.D.
Abstract
In the era where big data is the new norm, a higher emphasis has been placed on models which guarantees the release and exchange of data. The need for privacy-preserving data arose as more sophisticated data-mining techniques led to breaches of sensitive information. In this thesis, we present a secure multiparty protocol for the purpose of integrating multiple datasets simultaneously such that the contents of each dataset is not revealed to any of the data owners, and the contents of the integrated data do not compromise individual’s privacy. We utilize privacy by simulation to prove that the protocol is privacy-preserving, and we show that the output data satisfies ϵ-differential privacy.
DOI
10.18122/td/1390/boisestate
Recommended Citation
Harris, Anthony, "Secure MultiParty Protocol for Differentially-Private Data Release" (2018). Boise State University Theses and Dissertations. 1390.
10.18122/td/1390/boisestate
Included in
Discrete Mathematics and Combinatorics Commons, Information Security Commons, Number Theory Commons