東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Synthesizing Realistic Substitute Data for a Law Enforcement Database Using a Python Library.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Synthesizing Realistic Substitute Data for a Law Enforcement Database Using a Python Library./
Author:	Carrola, Anthony.
Description:	1 online resource (41 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
Subject:	Names. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29283489click for full text (PQDT)
ISBN:	9798845436689

Synthesizing Realistic Substitute Data for a Law Enforcement Database Using a Python Library.
Carrola, Anthony.

Synthesizing Realistic Substitute Data for a Law Enforcement Database Using a Python Library. - 1 online resource (41 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (M.Sc.)--West Virginia University, 2022.

Includes bibliographical references

In many databases, there is private or sensitive data that should not be accessible to any but a few individuals, such as HIPAA (Health Insurance Portability and Accountability Act) protected or LE (law enforcement) data. However, there is often a need to work with the data or change it for proper and thorough testing, especially for the developers . In some cases, the developers may be authorized to access and view the data, but it is rarely allowable for that data to be changed. Further, it is unlikely, especially on a large project, that all of the developers will have the authorization to view the data. In this case, it can be profitable to have easily creatable synthetic or 'fake' data to fill the database that mimics the real data enough to be used in all the same tests and to develop endpoints and APIs that will work with the real data. There are many possible ways to achieve this, such as shuffling the sensitive data information, or filling the sensitive data with garbled information. There are, however, drawbacks to such methods, as the data then becomes unwieldy or nonsensical to work with. Therefore, for this study, a Python library called Factory Boy, was used. Factory Boy can inherit the Django database models and then be used to generate randomized but realistic looking data, capable of mimicking all the complexities of actual database relationships and information.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798845436689Subjects--Topical Terms:

3680861
Names.
Index Terms--Genre/Form:

542853
Electronic books.

Synthesizing Realistic Substitute Data for a Law Enforcement Database Using a Python Library.
LDR:02753nmm a2200385K 4500 001 2356351
005 20230612102705.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798845436689
035 $a (MiAaPQ)AAI29283489
035 $a (MiAaPQ)WVirginia11332
035 $a AAI29283489
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Carrola, Anthony. $3 3696833
245 1 0 $a Synthesizing Realistic Substitute Data for a Law Enforcement Database Using a Python Library.
264 0 $c 2022
300 $a 1 online resource (41 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
500 $a Advisor: Valenti, Matthew C. ; Devine, Thomas.
502 $a Thesis (M.Sc.)--West Virginia University, 2022.
504 $a Includes bibliographical references
520 $a In many databases, there is private or sensitive data that should not be accessible to any but a few individuals, such as HIPAA (Health Insurance Portability and Accountability Act) protected or LE (law enforcement) data. However, there is often a need to work with the data or change it for proper and thorough testing, especially for the developers . In some cases, the developers may be authorized to access and view the data, but it is rarely allowable for that data to be changed. Further, it is unlikely, especially on a large project, that all of the developers will have the authorization to view the data. In this case, it can be profitable to have easily creatable synthetic or 'fake' data to fill the database that mimics the real data enough to be used in all the same tests and to develop endpoints and APIs that will work with the real data. There are many possible ways to achieve this, such as shuffling the sensitive data information, or filling the sensitive data with garbled information. There are, however, drawbacks to such methods, as the data then becomes unwieldy or nonsensical to work with. Therefore, for this study, a Python library called Factory Boy, was used. Factory Boy can inherit the Django database models and then be used to generate randomized but realistic looking data, capable of mimicking all the complexities of actual database relationships and information.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Names. $3 3680861
650 4 $a Generators. $3 3681674
650 4 $a Test systems. $3 3686152
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Law enforcement. $3 607408
650 4 $a Genetic algorithms. $3 533907
650 4 $a Internet crime. $3 3541387
650 4 $a Databases. $3 747532
650 4 $a Design. $3 518875
650 4 $a Data encryption. $3 3680528
650 4 $a Social security numbers. $3 3562918
650 4 $a Criminal investigations. $3 3564842
650 4 $a Computer science. $3 523869
650 4 $a Criminology. $3 533274
650 4 $a Law. $3 600858
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0389
690 $a 0206
690 $a 0800
690 $a 0984
690 $a 0627
690 $a 0398
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a West Virginia University. $3 1017532
773 0 $t Dissertations Abstracts International $g 84-03B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29283489 $z click for full text (PQDT)