MARC details
| 000 -LEADER |
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| fixed length control field |
05026ntm a2200373 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125105953.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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t||||fr|||| 000 0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220310s2021 my a|||frm||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009150(Local) |
| Qualifying information |
hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .T43 2021 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Thamer Khalil Esmeel, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Increasing data anonymity using privacy techniques and advanced encryption standard / |
| Statement of responsibility, etc. |
Thamer Khalil Esmeel |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2021 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Date of production, publication, distribution, manufacture, or copyright notice |
© 2021 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiv, 106 pages : |
| Other physical details |
llustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD ROM |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
unmediated |
| Source |
rdamedia |
| 337 ## - MEDIA TYPE |
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| Media type term |
computer |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
| Source |
rdacarrier |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
computer disc |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2021 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Data security and data privacy have been an important area in recent years. Dataset often consists of sensitive data fields, exposure of which may jeopardize individuals associated with the data. With the technological advancement, the attackers have been developing new methods from time to time to gain access to sensitive information from the bank, national and voter identification cards, etc. To resolve this issue, privacy techniques hinder the identification of the person through an increase of the anonymity of the individual in the dataset to protect sensitive information. This thesis addresses the problem of protecting sensitive user data in the dataset from privacy and security violations and malicious activities by attackers of the system using the integrated technique between privacy techniques with a security technique Advanced Encryption Standard (AES). The research objectives are to design an integrated technique between privacy techniques with a security technique for protecting data in the dataset. To implement the integrated technique between privacy techniques with security techniques to protect sensitive user data in the dataset. To validate the integrated technique by comparing the accuracy results before and after applying privacy techniques in the dataset using the data mining techniques. The research methodology consists of three phases. The first phase constitutes determining the suitable dataset and investigating the techniques and understanding of techniques and frameworks related to security and privacy. The second phase involves implementing the integrated technique of privacy techniques with AES. The third phase involves evaluating the classification results after experimenting with the implementation of the processes of the proposed technique. In this research, the security technique that was used is AES, and privacy techniques were differential privacy, k-Anonymity, Sample-uniqueness; and the data mining techniques: Naive Bayes, J48 and Neural Network were used under Weka. Many tests were conducted using Weka Experimenter Environment to classify the data to check the usefulness of the data for analysis purposes. Classification results before and after using privacy techniques were compared. It was found that differential privacy on the gender column of the dataset provides the best results in accuracy and outperforms the k-Anonymity technique and sample-uniqueness technique. However, kAnonymity and sample-uniqueness techniques on the age and cholesterol columns show better results in accuracy than the differential privacy technique. Besides, it was observed that for classification accuracy on Weka Experimenter Environment, Naïve Bayes presented better results than Neural Network and J48. To avert the dataset from external attackers, AES was used to encrypt the dataset passed through the privacy technique. To increase the safety, the encrypted file the general key involved with the encryption was split into five files and then each subfile and the subkey were attached and stores in five servers. The test results demonstrate the success of using the integrated technique using privacy techniques with the security technique to protect sensitive user data in the dataset. The future work can be focused on enhancing the classification accuracy of the protection schemes by examining more privacy techniques and applying them to big data and carrying out experiments on the big data in the cloud. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |