서브메뉴
검색
Topics in Privacy, Data Privacy and Differential Privacy
Topics in Privacy, Data Privacy and Differential Privacy
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103535
- ISBN
- 9798280711068
- DDC
- 310
- 저자명
- Bailie, James.
- 서명/저자
- Topics in Privacy, Data Privacy and Differential Privacy
- 발행사항
- [Sl] : Harvard University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 465 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Meng, Xiao-Li.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2025.
- 초록/해제
- 요약In an era of unprecedented data availability and analytic capacity, the protection of individuals' privacy in statistical data releases is becoming an increasingly difficult problem. This dissertation contributes to the theoretical and methodological foundations of statistical data privacy, largely focusing on differential privacy (DP). We begin with a multifaceted investigation into privacy from legal, economic, social, and philosophical standpoints, before turning to a formal system of DP specifications built around five core building blocks found throughout the literature: the domain, multiverse, input premetric, output premetric, and protection loss budget. This system is applied to statistical disclosure control (SDC) mechanisms used in the US Decennial Census, analyzing both the traditional method of data swapping and the contemporary TopDown Algorithm. Beyond these case studies, this dissertation explores the inferential limitations posed by DP and Pufferfish privacy in both frequentist and Bayesian settings, establishing general bounds under mild assumptions. It further addresses the challenges of applying DP to complex survey pipelines, incorporating issues such as sampling, weighting, and imputation. Finally, it contextualizes DP within broader frameworks of data privacy, namely the Five Safes and contextual integrity, advocating for a more integrated approach to privacy that respects statistical utility, transparency, and societal norms.
- 일반주제명
- Statistics
- 일반주제명
- Mathematics
- 일반주제명
- Applied mathematics
- 키워드
- Data swapping
- 기타저자
- Harvard University Statistics
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017357603
■00520260202103535
■006m o d
■007cr#unu||||||||
■020 ▼a9798280711068
■035 ▼a(MiAaPQ)AAI32040385
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aBailie, James.▼0(orcid)0000-0002-9301-2961
■24510▼aTopics in Privacy, Data Privacy and Differential Privacy
■260 ▼a[Sl]▼bHarvard University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a465 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Meng, Xiao-Li.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2025.
■520 ▼aIn an era of unprecedented data availability and analytic capacity, the protection of individuals' privacy in statistical data releases is becoming an increasingly difficult problem. This dissertation contributes to the theoretical and methodological foundations of statistical data privacy, largely focusing on differential privacy (DP). We begin with a multifaceted investigation into privacy from legal, economic, social, and philosophical standpoints, before turning to a formal system of DP specifications built around five core building blocks found throughout the literature: the domain, multiverse, input premetric, output premetric, and protection loss budget. This system is applied to statistical disclosure control (SDC) mechanisms used in the US Decennial Census, analyzing both the traditional method of data swapping and the contemporary TopDown Algorithm. Beyond these case studies, this dissertation explores the inferential limitations posed by DP and Pufferfish privacy in both frequentist and Bayesian settings, establishing general bounds under mild assumptions. It further addresses the challenges of applying DP to complex survey pipelines, incorporating issues such as sampling, weighting, and imputation. Finally, it contextualizes DP within broader frameworks of data privacy, namely the Five Safes and contextual integrity, advocating for a more integrated approach to privacy that respects statistical utility, transparency, and societal norms.
■590 ▼aSchool code: 0084.
■650 4▼aStatistics
■650 4▼aMathematics
■650 4▼aApplied mathematics
■653 ▼aDifferential privacy
■653 ▼aStatistical disclosure control
■653 ▼aData swapping
■653 ▼aBayesian settings
■653 ▼aContextual integrity
■690 ▼a0463
■690 ▼a0405
■690 ▼a0364
■71020▼aHarvard University▼bStatistics.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357603▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
