Loading...
Methods to achieve t-closeness for privacy preserving data publishing
Gowda, Vikas Thammanna
Gowda, Vikas Thammanna
Files
Loading...
dissertation
Adobe PDF, 1007.35 KB
Authors
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2023-05
Type
Dissertation
Genre
Keywords
Subjects (LCSH)
Electronic dissertations
Citation
Abstract
Privacy Preserving Data Publishing is an area of research focused on developing
methods of anonymizing sensitive relational data such that it can be published without
compromising the privacy of the individuals the data represents. The t-closeness technique
is one of the most popular techniques for preserving individual privacy in data. It involves
generalizing and suppressing some attributes of a given table, after partitioning the set of
all records of that table into equivalence classes that satisfy a certain constraint.
We present three methods for anonymizing datasets addressing the drawbacks of the
existing methods. We present a new method to partition the set of records of a table into
such equivalence classes. The rst method has several advantages over the existing methods
for this task. The classes generated by our method are near-optimal, in that they satisfy the
t-closeness constraint for even the \smallest" t value for which t-closeness is achievable and
useful for the given table, thereby providing the highest amount of privacy.
The second method anonymizes data with multiple sensitive attributes such that the
privacy parameter t for each can be selected individually. Our method partitions the data
into fragments and selects appropriate numbers of records from each fragment to create
equivalence classes with sensitive attribute distributions that are guaranteed t-close. Our
method can easily be generalized to an arbitrary number of sensitive attributes and to
sensitive attributes with continuous domains.
In the third method we present an algorithm for generating equivalence classes in the
presence of multiple sensitive attributes. The equivalence classes generated by our method
satisfy t-closeness for even the smallest t value for which t-closeness is achievable and useful
for the given dataset, thereby providing the highest possible amount of privacy.
Table of Contents
Description
Thesis (Ph.D.)-- Wichita State University, College of Engineering, School of Computing
Publisher
Wichita State University
Journal
Book Title
Series
Digital Collection
Finding Aid URL
Use and Reproduction
© Copyright 2023 by Vikas Thammanna Gowda
All Rights Reserved