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Privacy-preserving collaborative optimization

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TitleInfo
Title
Privacy-preserving collaborative optimization
Name (type = personal)
NamePart (type = family)
Hong
NamePart (type = given)
Yuan
NamePart (type = date)
1983-
DisplayForm
Yuan Hong
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Vaidya
NamePart (type = given)
Jaideep
DisplayForm
Jaideep Vaidya
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Adam
NamePart (type = given)
Nabil
DisplayForm
Nabil Adam
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Atluri
NamePart (type = given)
Vijayalakshmi
DisplayForm
Vijayalakshmi Atluri
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Clifton
NamePart (type = given)
Christopher W.
DisplayForm
Christopher W. Clifton
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - Newark
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2013
DateOther (qualifier = exact); (type = degree)
2013-10
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
With the rapid growth of computing, storing and networking resources, data is not only collected and stored, but also analyzed by different parties. This creates serious privacy problems while inhibiting the use of such distributed data. In turn, this raises the question of whether it is possible to realize value from distributed data without violating security and privacy concerns. Privacy-preserving data analysis has attracted considerable attention in recent years. Specifically, classification, clustering, association rule mining, outlier detection, regression among others, are securely implemented to analyze data privately held by multiple parties. The basic premise of such secure data analysis is that only the data analysis result can be revealed. As a fundamental problem found in many diverse fields, optimization is the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables within an allowed set. Inspired from multiparty data analysis, the ubiquitous collection of data opens even greater opportunities in the optimization problems, applicable to the fields of operations research, computer science and mathematics. Collaborative optimization, when done properly with distributed data from different organizations, can facilitate them to improve the allocation of global resources without compromising on security. The primary goal of this dissertation is to develop privacy-preserving collaborative optimization techniques that would allow organizations to gain the maximum value from local information without (or with limited) information disclosure. While answering this problem, an inherent aim is to solve these fundamental problems underlying privacy-preserving analysis and secure multiparty computation (SMC) while making it more accessible and applicable. In this dissertation, we look at fundamental optimization problems such as linear programming, non-linear programming and some classic NP-hard problems. Particularly, we discuss the potential security and privacy concern in the collaborative formulations of them, which occur in real world, such as logistics and scheduling in supply chain management. To securely solve them, we present efficient privacy-preserving methods along with formal security analysis for the proposed privacy notions. In addition, we identify a potential attack to an earlier work and amend the transformation method with enhanced security guarantee. We also address how game theoretic techniques can be used to solve some of the fundamental incentive problems underlying secure multiparty computation in collaborative optimization. The computation/communication cost analysis and the experimental results demonstrate the feasibility, applicability and scalability of the proposed approaches.
Subject (authority = RUETD)
Topic
Management
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_4911
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xii, 203 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Yuan Hong
Subject (authority = ETD-LCSH)
Topic
Data protection
Subject (authority = ETD-LCSH)
Topic
Electronic data processing departments--Security measures
Subject (authority = ETD-LCSH)
Topic
Data collection platforms--Security measures
Subject (authority = ETD-LCSH)
Topic
Privacy, Right of
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)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3FT8J1H
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Hong
GivenName
Yuan
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-07-15 23:16:48
AssociatedEntity
Name
Yuan Hong
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - Newark
AssociatedObject
Type
License
Name
Author Agreement License
Detail
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
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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