LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract
Data analysts use outlier analysis to discover non-conforming patterns in data to gen- erate actionable insights. It is an incredibly useful approach, but like all data-driven approaches, it raises privacy-related serious ethical and legal concerns when data is about peoples’ information. Is it possible to accurately analyze data for outliers while protecting the privacy of people whose data we analyze? In this dissertation, we explicate methods to answer this question for the most practically relevant case, where outliers are defined in a data-dependent way and current privacy methods such as differential privacy fail to achieve practically meaningful utility.
To define what it means to protect privacy in outlier analysis, we conceptualize sensitive privacy — it not only admits efficient algorithmic constructions but is also amenable to analysis. We introduce novel constructions to develop sensitively private mechanisms to accurately identify outliers, and to compile low-accuracy differentially private mechanisms into high-accuracy sensitively private mechanisms. Furthermore, to address the lack of a principled approach to private outlier analysis, we provide a framework to help a data analyst identify the right problem-specification and a practical solution for her application.
Finally, we develop mechanisms — which guarantee privacy and practically mean- ingful utility — to identify (β,r)-anomalies as well as covid-19 hotspots (an outlying event). An extensive empirical evaluation of these private mechanisms over a range of real-world datasets and use cases overwhelmingly supports the effectiveness of our approach.
Subject (authority = RUETD)
Topic
Management
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_11406
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (ix, 140 pages)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Genre (authority = ExL-Esploro)
ETD doctoral
RelatedItem (type = host)
TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
Identifier (type = local)
rucore10002600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top
Technical
RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.4
ApplicationName
macOS Version 10.15.7 (Build 19H2) Quartz PDFContext