Online Archive of University of Virginia Scholarship
Machine Learning: Integrating Security Techniques in Curriculum / Front End Protection: How the GDPR Is Enforced271 views
Author
Teefey, Maclay, School of Engineering and Applied Science, University of Virginia
Advisors
Norton, Peter, EN-Engineering and Society, University of Virginia
Vrugtman, Rosanne, EN-Comp Science Dept, University of Virginia
Morrison, Briana, EN-Comp Science Dept, University of Virginia
Abstract
How can online private information be protected? Private information leaks cost businesses millions and can expose individuals to grave risks. Data security protection measures and enforced regulations are needed.
Machine Learning models have unique vulnerabilities that are not covered as part of UVA machine learning or cybersecurity classes at the University of Virginia. A new special topics course is therefore proposed. In it, students would study the security faults in machine learning models and learn to detect and prevent such threats.
In 2018, the European Union enacted the General Data Protection Regulation (GDPR), a privacy standard for all member states, and established Data Protection Agencies (DPAs) for each member state. Privacy advocacy groups and industry groups contend that too many companies do not comply with the GDPR. They seek stricter enforcement by pressuring DPAs to punish violators and by helping companies comply with the GDPR.
Degree
BS (Bachelor of Science)
Keywords
Machine Learning; Data Security; GDPR; Security; General Data Protection Regulation
Teefey, Maclay. Machine Learning: Integrating Security Techniques in Curriculum / Front End Protection: How the GDPR Is Enforced. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2023-05-08, https://doi.org/10.18130/81q8-vb68.
