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Data Masking, Encryption, and their Effect on Classification Performance : = Trade-offs Between Data Security and Utility.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Data Masking, Encryption, and their Effect on Classification Performance :/
其他題名:	Trade-offs Between Data Security and Utility.
作者:	Asenjo, Juan C.
面頁冊數:	1 online resource (187 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: A.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355104165

Data Masking, Encryption, and their Effect on Classification Performance : = Trade-offs Between Data Security and Utility.
Asenjo, Juan C.

Data Masking, Encryption, and their Effect on Classification Performance :Trade-offs Between Data Security and Utility. - 1 online resource (187 pages)

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: A.

Thesis (Ph.D.)--Nova Southeastern University, 2017.

Includes bibliographical references

As data mining increasingly shapes organizational decision-making, the quality of its results must be questioned to ensure trust in the technology. Inaccuracies can mislead decision-makers and cause costly mistakes. With more data collected for analytical purposes, privacy is also a major concern. Data security policies and regulations are increasingly put in place to manage risks, but these policies and regulations often employ technologies that substitute and/or suppress sensitive details contained in the data sets being mined. Data masking and substitution and/or data encryption and suppression of sensitive attributes from data sets can limit access to important details. It is believed that the use of data masking and encryption can impact the quality of data mining results. This dissertation investigated and compared the causal effects of data masking and encryption on classification performance as a measure of the quality of knowledge discovery. A review of the literature found a gap in the body of knowledge, indicating that this problem had not been studied before in an experimental setting. The objective of this dissertation was to gain an understanding of the trade-offs between data security and utility in the field of analytics and data mining. The research used a nationally recognized cancer incidence database, to show how masking and encryption of potentially sensitive demographic attributes such as patients' marital status, race/ethnicity, origin, and year of birth, could have a statistically significant impact on the patients' predicted survival. Performance parameters measured by four different classifiers delivered sizable variations in the range of 9% to 10% between a control group, where the select attributes were untouched, and two experimental groups where the attributes were substituted or suppressed to simulate the effects of the data protection techniques. In practice, this represented a corroboration of the potential risk involved when basing medical treatment decisions using data mining applications where attributes in the data sets are masked or encrypted for patient privacy and security concerns.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355104165Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

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