Date of Final Oral Examination (Defense)
Type of Culminating Activity
Master of Science in Computer Science
Gaby Dagher, Ph.D.
Jyh-haw Yeh, Ph.D.
Yantian Hou, Ph.D.
Privacy-preserving data publishing is a mechanism for sharing data while ensuring the privacy of individuals is preserved in the published data and utility is maintained for data mining and analysis. There is a huge need for sharing genomic data to advance medical and health research. However, since genomic data is highly sensitive and the ultimate identifier, it is a big challenge to publish genomic data while protecting the privacy of individuals in the data.
In this thesis, we address the aforementioned challenge by presenting an approach for privacy-preserving genomic data publishing via differentially-private suffix tree. The proposed algorithm uses a top-down approach and utilizes Laplace mechanism to divide the raw genomic data into disjoint partitions, and then normalize the partitioning structure to ensure consistency and maintain utility. The output of our algorithm is a differentially-private suffix tree, a data structure most suitable for efficient search on genomic data. We experiment on real-life genomic data obtained from the Human Genome Privacy Challenge project, and we show that our approach is efficient, scalable, and achieves high utility with respect to genomic sequence matching count queries.
Khatri, Tanya, "Privacy-Preserving Genomic Data Publishing via Differential Privacy" (2018). Boise State University Theses and Dissertations. 1481.
Available for download on Saturday, December 19, 2020