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The Graduate School and University Center of The City University of New York

Efficient Private Information Retrieval

Abstract

dc:description.abstract

<p>A vast amount of today's Internet users' on line activities consists of queries to various types of databases. From traditional search engines to modern cloud based services, a person's everyday queries over a period of time on various data sources, will leave a trail visible to the query processor, which can reveal significant and possibly sensitive information about her. Private Information Retrieval (PIR) algorithms can be leveraged for providing perfect privacy to users' queries, though at a restrictive computational cost. In this work, we consider today's highly distributed computing environments, as well as certain secure-hardware devices, for optimizing existing PIR solutions. In particular, we initially employ available secure-hardware in a novel approach with the goal of providing faster and constant private query responses, by sacrificing some degree of privacy. Further on, we utilize the widely used Message Passing Interface (MPI) protocol for designing a library which can be used in third party software for performing private queries.</p>

Degree

thesis:*
Name thesis:degree_name
Doctor of Philosophy
Level thesis:degree_level
Doctoral
Discipline thesis:degree_discipline
Computer Science
Grantor
The Graduate School and University Center of The City University of New York
Year dc:date.available
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Nikolopoulos, Konstantinos
Advisor dc:contributor.advisor
  • Spiridon Bakiras
Committee members dc:contributor.committeemember
  • Abdullah Uz Tansel
  • Sven Dietrich
  • Efstathios Zachos

Subjects

dc:subject × 4

Identifiers

dc:identifier.*
Repository record dc:identifier
https://academicworks.cuny.edu/gc_etds/3158
OAI identifier oai:identifier
oai:academicworks.cuny.edu:gc_etds-4212

Chain of custody

source
Harvested from
City University of New York - Graduate Center
Base URL
academicworks.cuny.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Nikolopoulos, Konstantinos. Efficient Private Information Retrieval. Doctoral thesis, The Graduate School and University Center of The City University of New York, 2019. https://academicworks.cuny.edu/gc_etds/3158