Date of Final Oral Examination (Defense)
Type of Culminating Activity
Master of Science in Mathematics
Gaby Dagher, Ph.D.
Lijana Babinkostova, Ph.D.
Marion Scheepers, Ph.D.
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.
Harris, Anthony, "Secure MultiParty Protocol for Differentially-Private Data Release" (2018). Boise State University Theses and Dissertations. 1390.