{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125593"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125593","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Human factors in secure and non-abusive machine learning systems","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_has_math":false,"creators":["Mink, Jaron Maurice"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Wang, Gang","Redmiles, Elissa M","Gunter, Carl","Cobb, Camille"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-10","date_published":"2024-07-10","updated_at":"2026-07-22T22:25:02Z","subjects":["Machine Learning","Deepfake","Security","Privacy","Adversarial Machine Learning","Trustworthy Machine Learning","Usable Security","Human Factors","Secure Machine Learning","Non-abusive Machine Learning","Human-ai Interaction","Human-centric Machine Learning","Human-centric Ai","Human-ml Interaction"],"languages":["en","eng"],"rights":["Copyright 2024 Jaron Mink"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125593","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Gang","Redmiles, Elissa M","Gunter, Carl","Cobb, Camille"]},{"key":"dc:creator","label":"Author","values":["Mink, Jaron Maurice"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-10","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Deepfake","Security","Privacy","Adversarial Machine Learning","Trustworthy Machine Learning","Usable Security","Human Factors","Secure Machine Learning","Non-abusive Machine Learning","Human-ai Interaction","Human-centric Machine Learning","Human-centric Ai","Human-ml Interaction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Jaron Mink"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125593"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Jaron Mink, accepted the attached license on 2024-07-09 at 13:11.","The student, Jaron Mink, submitted this Dissertation for approval on 2024-07-09 at 13:33.","This Dissertation was approved for publication on 2024-07-10 at 12:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21012 on 2025-02-04 at 21:04:46","Today, a significant portion of mission-critical work traditionally done by humans (e.g., driving cars, approving loans, medical triaging) is on the verge of being replaced by machine learning (ML). Historically, not considering human interactions with conventional software systems has led to significant harm. This is even more true for emerging ML systems as there is a lack of principled methods to construct safe and secure human-ML interaction paradigms. To prevent similar harm in ML-based systems, it is paramount that we understand vulnerabilities and apply safeguards now, while they are being designed and deployed. This dissertation investigates how the interaction of human factors and ML systems results in security implications via two perspectives. Specifically, this dissertation investigates how human factors can be exploited by ML-enabled abuse to reduce security in the context of deepfake deception (Chapter 3 and Chapter 4) and harnessed to improve the security of ML systems in the context of ML-enabled analysts tools (Chapter 5), and application of adversarial ML defenses (Chapter 6). In summary, these works show how human factors and perspectives contribute to the security of ML systems and that accounting for such interaction is necessary to protect from adversarial exploits."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Human factors in secure and non-abusive machine learning systems"]}]}],"canonical_facts":{"dc:contributor":["Wang, Gang","Redmiles, Elissa M","Gunter, Carl","Cobb, Camille"],"dc:creator":["Mink, Jaron Maurice"],"dc:date":["2024-07-10","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Jaron Mink, accepted the attached license on 2024-07-09 at 13:11.","The student, Jaron Mink, submitted this Dissertation for approval on 2024-07-09 at 13:33.","This Dissertation was approved for publication on 2024-07-10 at 12:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21012 on 2025-02-04 at 21:04:46","Today, a significant portion of mission-critical work traditionally done by humans (e.g., driving cars, approving loans, medical triaging) is on the verge of being replaced by machine learning (ML). Historically, not considering human interactions with conventional software systems has led to significant harm. This is even more true for emerging ML systems as there is a lack of principled methods to construct safe and secure human-ML interaction paradigms. To prevent similar harm in ML-based systems, it is paramount that we understand vulnerabilities and apply safeguards now, while they are being designed and deployed. This dissertation investigates how the interaction of human factors and ML systems results in security implications via two perspectives. Specifically, this dissertation investigates how human factors can be exploited by ML-enabled abuse to reduce security in the context of deepfake deception (Chapter 3 and Chapter 4) and harnessed to improve the security of ML systems in the context of ML-enabled analysts tools (Chapter 5), and application of adversarial ML defenses (Chapter 6). In summary, these works show how human factors and perspectives contribute to the security of ML systems and that accounting for such interaction is necessary to protect from adversarial exploits."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125593"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Jaron Mink"],"dc:subject":["Machine Learning","Deepfake","Security","Privacy","Adversarial Machine Learning","Trustworthy Machine Learning","Usable Security","Human Factors","Secure Machine Learning","Non-abusive Machine Learning","Human-ai Interaction","Human-centric Machine Learning","Human-centric Ai","Human-ml Interaction"],"dc:title":["Human factors in secure and non-abusive machine learning systems"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}