Statistical analysis of identity risk of exposure and cost using the ecosystem of identity attributes
| dc.contributor.advisor | Barber, K. Suzanne | |
| dc.creator | Chen, Chia-Ju | |
| dc.date.accessioned | 2020-03-20T21:03:21Z | |
| dc.date.available | 2020-03-20T21:03:21Z | |
| dc.date.created | 2019-12 | |
| dc.date.issued | 2019-11-04 | |
| dc.date.submitted | December 2019 | |
| dc.date.updated | 2020-03-20T21:03:21Z | |
| dc.description.abstract | Personally Identifiable Information (PII) is often called the "currency of the Internet" as identity assets are collected, shared, sold, and used for almost every transaction on the Internet. PII is used for all types of applications from access control to credit score calculations to targeted advertising. Every market sector relies on PII to know and authenticate their customers and their employees. With so many businesses and government agencies relying on PII to make important decisions and so many people being asked to share personal data, it is critical to better understand the fundamentals of identity to protect it and responsibly use it. Previously developed comprehensive Identity Ecosystem utilizes graphs to model PII assets and their relationships and is powered by empirical data from almost 6,000 real-world identity theft and fraud news reports to populate the UT CID Identity Ecosystem. We analyze UT CID Identity Ecosystem using graph theory and report numerous novel statistics using identity asset content, structure, value, accessibility, and impact. Our work sheds light on how identity is used and paves the way for improving identity protection. | |
| dc.description.department | Electrical and Computer Engineering | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/2152/80409 | |
| dc.identifier.uri | http://dx.doi.org/10.26153/tsw/7425 | |
| dc.language.iso | en | |
| dc.subject | Security | |
| dc.subject | Social network analysis | |
| dc.title | Statistical analysis of identity risk of exposure and cost using the ecosystem of identity attributes | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Electrical and Computer Engineering | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | The University of Texas at Austin | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science in Engineering |
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