RUcore: Rutgers University Community Repository
Data privacy in knowledge discovery
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
Data privacy in knowledge discovery
Identifier
ETD_2588
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000053097
Language
LanguageTerm
(authority = ISO639-2);
(type = code)
eng
Genre
(authority = marcgt)
theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Computer Science
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
Data protection
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Data mining
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Knowledge management--Computer networks--Security measures
Abstract
(type = abstract)
This thesis addresses data privacy in various stages of extracting knowledge embedded in databases. Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Legal and ethical considerations might require measures to protect an individual's privacy in any use or release of the data. In this thesis, we address the problem of preserving privacy in the two following cases: (1) in distributed knowledge discovery; (2) in situations where the output of a data mining algorithm could itself breach privacy. We present results in two different models, namely secure multiparty computation (SMC) and differential privacy. The first part of the thesis presents privacy preserving protocols in the SMC model. Secure multiparty computation involves the collaborative computation of functions based on inputs from multiple parties. The privacy goal is to ensure that all parties receive only the final output without any party learning anything beyond what can be inferred from the output. Within this framework we address the problem of preserving privacy in the preprocessing and the data mining stages of knowledge discovery in databases. For the preprocessing stage, we present private protocols for the imputation of missing data in a dataset that is shared between two parties. For the data mining stage, we introduce the notion of arbitrarily partitioned data that generalizes both horizontally and vertically partitioned data. We present a privacy-preserving protocol for k-means clustering of arbitrarily partitioned data. We also develop a new simple k-clustering algorithm that was designed to be converted into a communication-efficient protocol for private clustering. The second part of the thesis deals with privacy in situations where the output of a data mining algorithm could itself breach privacy. In this setting, we present private inference control protocols in the SMC model for On-line Analytical Processing systems. In the differential privacymodel, the goal is to provide access to a statistical database while preserving the privacy of every individual in the database, irrespective of any auxiliary information that may be available to the database client. Under this privacy model, we present a practical privacy preserving decision tree classifier using random decision trees.
PhysicalDescription
Form
(authority = gmd)
electronic resource
Extent
xix, 184 p. : ill.
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note
(type = degree)
Ph.D.
Note
Includes abstract
Note
Vita
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Geetha Jagannathan
Name
(ID = NAME-1);
(type = personal)
NamePart
(type = family)
Jagannathan
NamePart
(type = given)
Geetha
NamePart
(type = date)
1968-
Role
RoleTerm
(authority = RULIB)
author
DisplayForm
Geetha Jagannathan
Name
(ID = NAME-2);
(type = personal)
NamePart
(type = family)
Wright
NamePart
(type = given)
Rebecca N
Role
RoleTerm
(authority = RULIB)
chair
Affiliation
Advisory Committee
DisplayForm
Rebecca N Wright
Name
(ID = NAME-3);
(type = personal)
NamePart
(type = family)
Muthukrishnan
NamePart
(type = given)
Shan
Role
RoleTerm
(authority = RULIB)
internal member
Affiliation
Advisory Committee
DisplayForm
Shan Muthukrishnan
Name
(ID = NAME-4);
(type = personal)
NamePart
(type = family)
Marian
NamePart
(type = given)
Amelie
Role
RoleTerm
(authority = RULIB)
internal member
Affiliation
Advisory Committee
DisplayForm
Amelie Marian
Name
(ID = NAME-5);
(type = personal)
NamePart
(type = family)
Shmatikov
NamePart
(type = given)
Vitaly
Role
RoleTerm
(authority = RULIB)
outside member
Affiliation
Advisory Committee
DisplayForm
Vitaly Shmatikov
Name
(ID = NAME-1);
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
(ID = NAME-2);
(type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm
(authority = RULIB)
school
OriginInfo
DateCreated
(qualifier = exact)
2010
DateOther
(qualifier = exact);
(type = degree)
2010
Place
PlaceTerm
(type = code)
xx
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Location
PhysicalLocation
(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T3Q52PQS
Genre
(authority = ExL-Esploro)
ETD doctoral
Back to the top
Rights
RightsDeclaration
(AUTHORITY = GS);
(ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder
(ID = PRH-1);
(type = personal)
Name
FamilyName
Jagannathan
GivenName
Geetha
Role
Copyright Holder
RightsEvent
(ID = RE-1);
(AUTHORITY = rulib)
Type
Permission or license
DateTime
2010-04-13 17:12:27
AssociatedEntity
(ID = AE-1);
(AUTHORITY = rulib)
Role
Copyright holder
Name
Geetha Jagannathan
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
(ID = AO-1);
(AUTHORITY = rulib)
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.
Back to the top
Technical
ContentModel
ETD
MimeType
(TYPE = file)
application/pdf
MimeType
(TYPE = container)
application/x-tar
FileSize
(UNIT = bytes)
2457600
Checksum
(METHOD = SHA1)
60fb6db3dc302cba1ac8fb72621ef8dc311f881c
Back to the top
Statistical Profile