Fingerprinting Codes and Separating Hash Families. / Rochanakul, Penying.

2013.

Research output: ThesisDoctoral Thesis

Unpublished

Documents

Abstract

The thesis examines two related combinatorial objects, namely fingerprinting codes and separating hash families.
Fingerprinting codes are combinatorial objects that have been studied for more than 15 years due to their applications in digital data copyright protection and their combinatorial interest. Four well-known types of fingerprinting codes are studied in this thesis; traceability, identifiable parent property, secure frameproof and frameproof. Each type of code is named after the security properties it guarantees. However, the power of these four types of fingerprinting codes is limited by a certain condition. The first known attempt to go beyond that came out in the concept of two-level traceability codes, introduced by Anthapadmanabhan and Barg (2009). This thesis extends their work to the other three types of fingerprinting codes, so in this thesis four types of two-level fingerprinting codes are defined. In addition, the relationships between the different types of codes are studied. We propose some first explicit non-trivial con- structions for two-level fingerprinting codes and provide some bounds on the size of these codes.
Separating hash families were introduced by Stinson, van Trung, and Wei as a tool for creating an explicit construction for frameproof codes in 1998. In this thesis, we state a new definition of separating hash families, and mainly focus on improving previously known bounds for separating hash families in some special cases that related to fingerprinting codes. We improve upper bounds on the size of frameproof and secure frameproof codes under the language of separating hash families.
Original languageEnglish
QualificationPh.D.
Awarding Institution
Supervisors/Advisors
Thesis sponsors
  • Royal Thai Government
Award date1 Jan 2013
Publication statusUnpublished - 2013
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 13657808