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Usable and Ubiquitous Privacy-Aware Sensing Devices
Usable and Ubiquitous Privacy-Aware Sensing Devices
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103649
- ISBN
- 9798314875469
- DDC
- 621.3
- 서명/저자
- Usable and Ubiquitous Privacy-Aware Sensing Devices
- 발행사항
- [Sl] : University of Michigan, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 283 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: Sample, Alanson P.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2025.
- 초록/해제
- 요약The proliferation of smart devices is bringing us closer to a future where everyday objects can monitor users, anticipate their needs, and track health metrics. However, privacy concerns have significantly hindered the adoption of information-rich sensors, such as microphones and cameras, particularly in sensitive home environments like bedrooms and bathrooms---locations where critical self-care behaviors and health events, such as falls, are most likely to occur. To address these concerns, ubiquitous sensing technologies must be designed with principles from the usable privacy community, ensuring privacy guarantees that promote adoption and maximize real-world impact. This dissertation presents a Privacy by Design approach to sensor-level privacy across three key domains. First, I introduce privacy-preserving microphones that do not capture speech frequencies but instead leverage inaudible ultrasound to outperform traditional microphones in acoustic event recognition. These microphones can also transform captured signals into locality sensitive hashes, ensuring that privacy-invasive raw audio cannot be reconstructed. In real-world deployments, these privacy-aware microphones demonstrate on-device detection of urinary voiding---an important kidney health metric---using only inaudible frequencies. Second, I explore privacy-preserving cameras that utilize thermal imaging to sanitize personally identifiable information on-device. This approach enables critical machine learning and computer vision applications, such as fall detection, without compromising performance. Finally, I present novel privacy-aware sensing techniques that inherently prevent the capture of sensitive information, such as airborne sound, by leveraging alternative signal sources like surface-acoustic waves. These methods enable activity recognition in the home without invasive data collection. Together, these contributions demonstrate how privacy-aware sensing can bridge the gap between user privacy and real-world sensing applications, paving the way for safer, more adoptable smart home technologies.
- 일반주제명
- Computer engineering
- 일반주제명
- Computer science
- 일반주제명
- Electrical engineering
- 키워드
- Privacy
- 키워드
- Ultrasound
- 기타저자
- University of Michigan Computer Science & Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798314875469
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■035 ▼a(MiAaPQ)umichrackham006184
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aIravantchi, Yasha.
■24510▼aUsable and Ubiquitous Privacy-Aware Sensing Devices
■260 ▼a[Sl]▼bUniversity of Michigan▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a283 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: Sample, Alanson P.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2025.
■520 ▼aThe proliferation of smart devices is bringing us closer to a future where everyday objects can monitor users, anticipate their needs, and track health metrics. However, privacy concerns have significantly hindered the adoption of information-rich sensors, such as microphones and cameras, particularly in sensitive home environments like bedrooms and bathrooms---locations where critical self-care behaviors and health events, such as falls, are most likely to occur. To address these concerns, ubiquitous sensing technologies must be designed with principles from the usable privacy community, ensuring privacy guarantees that promote adoption and maximize real-world impact. This dissertation presents a Privacy by Design approach to sensor-level privacy across three key domains. First, I introduce privacy-preserving microphones that do not capture speech frequencies but instead leverage inaudible ultrasound to outperform traditional microphones in acoustic event recognition. These microphones can also transform captured signals into locality sensitive hashes, ensuring that privacy-invasive raw audio cannot be reconstructed. In real-world deployments, these privacy-aware microphones demonstrate on-device detection of urinary voiding---an important kidney health metric---using only inaudible frequencies. Second, I explore privacy-preserving cameras that utilize thermal imaging to sanitize personally identifiable information on-device. This approach enables critical machine learning and computer vision applications, such as fall detection, without compromising performance. Finally, I present novel privacy-aware sensing techniques that inherently prevent the capture of sensitive information, such as airborne sound, by leveraging alternative signal sources like surface-acoustic waves. These methods enable activity recognition in the home without invasive data collection. Together, these contributions demonstrate how privacy-aware sensing can bridge the gap between user privacy and real-world sensing applications, paving the way for safer, more adoptable smart home technologies.
■590 ▼aSchool code: 0127.
■650 4▼aComputer engineering
■650 4▼aComputer science
■650 4▼aElectrical engineering
■653 ▼aUbiquitous computing
■653 ▼aPrivacy
■653 ▼aSensing applications
■653 ▼aUltrasound
■690 ▼a0984
■690 ▼a0464
■690 ▼a0544
■71020▼aUniversity of Michigan▼bComputer Science & Engineering.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358138▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
