Through the increasingly common use of devices that provide ubiquitous sensor data such as wearables, mobile phones, and Internet-connected devices of the sort, privacy challenges are becoming even more significant. One major challenge that requires more focus is bystanders' privacy, as there are too few solutions that solve the issue. Of the solutions available, many of them do not give bystanders a choice in how their private data is used, Bystanders' privacy has become an afterthought when it comes to data capture in the forms of photographs, videos, voice recordings, etc. and continues to remain that way. This thesis provides a solution to enhance bystanders' facial privacy by developing a wearable device called FacePET that provides a way for bystanders to protect their privacy and give consent. FacePET was evaluated using experiments to detect faces in photos when users wore the device and by performing a usability study with 21 participants. We found that FacePET was successfully able to block 15 of the 21 participants' faces, yielding a success percentage of 71%. We found through the usability study that a majority of the participants would be willing to use FacePET, or a similar device, daily for their facial privacy protection.
","abstract_html":"<p>Through the increasingly common use of devices that provide ubiquitous sensor data such as wearables, mobile phones, and Internet-connected devices of the sort, privacy challenges are becoming even more significant. One major challenge that requires more focus is bystanders' privacy, as there are too few solutions that solve the issue. Of the solutions available, many of them do not give bystanders a choice in how their private data is used, Bystanders' privacy has become an afterthought when it comes to data capture in the forms of photographs, videos, voice recordings, etc. and continues to remain that way. This thesis provides a solution to enhance bystanders' facial privacy by developing a wearable device called FacePET that provides a way for bystanders to protect their privacy and give consent. FacePET was evaluated using experiments to detect faces in photos when users wore the device and by performing a usability study with 21 participants. We found that FacePET was successfully able to block 15 of the 21 participants' faces, yielding a success percentage of 71%. We found through the usability study that a majority of the participants would be willing to use FacePET, or a similar device, daily for their facial privacy protection.</p>","abstract_has_math":false,"creators":["Griffith, Scott"],"institution":null,"degree_name":"Computer Science - Applied Computing Track","degree_level":"Thesis","degree_discipline":"TSYS School of Computer Science","degree_department":null,"school":null,"contributors":["Dr. Alfredo Perez","Dr. Yesem Peker","Dr. Lydia Ray"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-01-01T08:00:00Z","date_published":"2019-01-01T08:00:00Z","updated_at":"2026-07-24T01:45:01Z","subjects":["Bystanders' Privacy","Face Detection","Face Recognition","Privacy","Wearables","Internet Of Things","Computer Sciences","Information Security"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://csuepress.columbusstate.edu/theses_dissertations/354","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Alfredo Perez","Dr. Yesem Peker","Dr. Lydia Ray"]},{"key":"dc:creator","label":"Author","values":["Griffith, Scott"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-01-30T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["TSYS School of Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Computer Science - Applied Computing Track"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bystanders' Privacy","Face Detection","Face Recognition","Privacy","Wearables","Internet Of Things","Computer Sciences","Information Security"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://csuepress.columbusstate.edu/theses_dissertations/354"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Through the increasingly common use of devices that provide ubiquitous sensor data such as wearables, mobile phones, and Internet-connected devices of the sort, privacy challenges are becoming even more significant. One major challenge that requires more focus is bystanders' privacy, as there are too few solutions that solve the issue. Of the solutions available, many of them do not give bystanders a choice in how their private data is used, Bystanders' privacy has become an afterthought when it comes to data capture in the forms of photographs, videos, voice recordings, etc. and continues to remain that way. This thesis provides a solution to enhance bystanders' facial privacy by developing a wearable device called FacePET that provides a way for bystanders to protect their privacy and give consent. FacePET was evaluated using experiments to detect faces in photos when users wore the device and by performing a usability study with 21 participants. We found that FacePET was successfully able to block 15 of the 21 participants' faces, yielding a success percentage of 71%. We found through the usability study that a majority of the participants would be willing to use FacePET, or a similar device, daily for their facial privacy protection.
"]},{"key":"dc:title","label":"Title","values":["Design and Evaluation of a Wearable System for Facial Privacy"]}]}],"canonical_facts":{"dc:contributor":["Dr. Alfredo Perez","Dr. Yesem Peker","Dr. Lydia Ray"],"dc:creator":["Griffith, Scott"],"dc:date.available":["2020-01-30T08:00:00Z"],"dc:description.abstract":["Through the increasingly common use of devices that provide ubiquitous sensor data such as wearables, mobile phones, and Internet-connected devices of the sort, privacy challenges are becoming even more significant. One major challenge that requires more focus is bystanders' privacy, as there are too few solutions that solve the issue. Of the solutions available, many of them do not give bystanders a choice in how their private data is used, Bystanders' privacy has become an afterthought when it comes to data capture in the forms of photographs, videos, voice recordings, etc. and continues to remain that way. This thesis provides a solution to enhance bystanders' facial privacy by developing a wearable device called FacePET that provides a way for bystanders to protect their privacy and give consent. FacePET was evaluated using experiments to detect faces in photos when users wore the device and by performing a usability study with 21 participants. We found that FacePET was successfully able to block 15 of the 21 participants' faces, yielding a success percentage of 71%. We found through the usability study that a majority of the participants would be willing to use FacePET, or a similar device, daily for their facial privacy protection.
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