Abstract
dc:description.abstractGroup testing is a method of designing collections of samples of individual items and assessing them as collections (rather than individuals) with the goal of revealing individuals who are positive for some attribute. It has been used to test for highly contagious diseases, such as coronavirus in recent years, often with the goal of minimizing processing time or the number of tests. Privacy of personal information is important, particularly when it comes to medical history or test results for diseases. Our research studies group testing designed with parity-check matrices of Hamming codes and constant row weight d-disjunct matrices. We consider partial knowledge that an eavesdropper needs to know from the group testing matrix to obtain personal medical data. We also evaluate the leakage risk of the information under certain assumptions of the eavesdropper's abilities. The thesis concludes by proposing future directions such as handling noise, correlated individuals, and decentralized testing designs.
Degree
thesis:*- Name thesis:degree_name
- Master of Science
- Level thesis:degree_level
- masters
- Discipline thesis:degree_discipline
- Mathematics
- Department dc:contributor.department
- Mathematics
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2025
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Liu, Shuqi
- Chair dc:contributor.committeechair
-
- Matthews, Gretchen L.
- Committee members dc:contributor.committeemember
-
- McMillon, Emily
- López, Hiram H.
- LeGrow, Jason T.
Subjects
dc:subject × 4Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- https://hdl.handle.net/10919/135506
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/135506