大葉大學圖書館 |

Database anonymization :privacy models, data utility, and microaggregation-based inter-model connections /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	658.478
書名/作者:	Database anonymization : : privacy models, data utility, and microaggregation-based inter-model connections // Josep Domingo- Ferrer, David Sa?nchez, and Jordi Soria-Comas, Universitat Rovira i Virgili, Tarragona, Catalonia
作者:	Domingo-Ferrer, Josep,
其他作者:	Sa?nchez, David
面頁冊數:	1 online resource (xv, 120 pages) : : illustrations
標題:	Data protection
標題:	Database security - Congresses.
ISBN:	9781627058445
ISBN:	1627058443
書目註:	Includes bibliographical references (pages 109-118)
內容註:	1. Introduction
摘要、提要註:	The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature
電子資源:	http://portal.igpublish.com/iglibrary/search/MCPB0000810.html

Database anonymization :privacy models, data utility, and microaggregation-based inter-model connections /
Domingo-Ferrer, Josep,

Database anonymization :privacy models, data utility, and microaggregation-based inter-model connections /Josep Domingo- Ferrer, David Sa?nchez, and Jordi Soria-Comas, Universitat Rovira i Virgili, Tarragona, Catalonia - 1 online resource (xv, 120 pages) :illustrations - Synthesis lectures on information security, privacy, & trust, #151945-9750 ;. - Synthesis lectures on information security, privacy and trust ; #15..

Includes bibliographical references (pages 109-118)

1. Introduction

The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature

ISBN: 9781627058445

Standard No.: 10.2200 / S00690ED1V01Y201512SPT015doiSubjects--Topical Terms:

164601
Data protection
Subjects--Index Terms:

data releasesIndex Terms--Genre/Form:

344515
Electronic books

LC Class. No.: HF5548.37 / .D653 2016

Dewey Class. No.: 658.478

Database anonymization :privacy models, data utility, and microaggregation-based inter-model connections /
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490 0 $a Synthesis lectures on information security, privacy, & trust, $x 1945-9750 ; $v #15
504 $a Includes bibliographical references (pages 109-118)
505 0 $a 1. Introduction
505 8 $a 2. Privacy in data releases -- 2.1 Types of data releases -- 2.2 Microdata sets -- 2.3 Formalizing privacy -- 2.4 Disclosure risk in microdata sets -- 2.5 Microdata anonymization -- 2.6 Measuring information loss -- 2.7 Trading off information loss and disclosure risk -- 2.8 Summary
505 8 $a 3. Anonymization methods for microdata -- 3.1 Non-perturbative masking methods -- 3.2 Perturbative masking methods -- 3.3 Synthetic data generation -- 3.4 Summary
505 8 $a 4. Quantifying disclosure risk: record linkage -- 4.1 Threshold -based record linkage -- 4.2 Rule-based record linkage -- 4.3 Probabilistic record linkage -- 4.4 Summary
505 8 $a 5. The k-anonymity privacy model -- 5.1 Insufficiency of data de-identification -- 5.2 The k-anonymity model -- 5.3 Generalization and suppression based k-anonymity -- 5.4 Microaggregation-based k-anonymity -- 5.5 Probabilistic k- anonymity -- 5.6 Summary
505 8 $a 6. Beyond k-anonymity: l-diversity and t -closeness -- 6.1 l- diversity -- 6.2 t-closeness -- 6.3 Summary
505 8 $a 7. t-closeness through microaggregation -- 7.1 Standard microaggregation and merging -- 7.2 t-closeness aware microaggregation: k-anonymity-first -- 7.3 t-closeness aware microaggregation: t-closeness-first -- 7.4 Summary
505 8 $a 8. Differential privacy -- 8.1 Definition -- 8.2 Calibration to the global sensitivity -- 8.3 Calibration to the smooth sensitivity -- 8.4 The exponential mechanism -- 8.5 Relation to k -anonymity-based models -- 8.6 Differentially private data publishing -- 8.7 Summary
505 8 $a 9. Differential privacy by multivariate microaggregation -- 9.1 Reducing sensitivity via prior multivariate microaggregation -- 9.2 Differentially private data sets by insensitive microaggregation -- 9.3 General insensitive microaggregation -- 9.4 Differential privacy with categorical attributes -- 9.5 A semantic distance for differential privacy -- 9.6 Integrating heterogeneous attribute types -- 9.7 Summary
505 8 $a 10. Differential privacy by individual ranking microaggregation -- 10.1 Limitations of multivariate microaggregation -- 10.2 Sensitivity reduction via individual ranking -- 10.3 Choosing the microggregation parameter k -- 10.4 Summary
505 8 $a 11. Conclusions and research directions -- 11.1 Summary and conclusions -- 11.2 Research directions -- Bibliography -- Authors' biographies
520 $a The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature
520 $a Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guarantees they offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization methods can be used to enforce privacy models and thereby offer ex ante privacy guarantees). These latter topics are relevant to researchers and advanced practitioners, who will gain a deeper understanding on the available data anonymization solutions and the privacy guarantees they can offer
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653 $a anonymization
653 $a privacy models
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653 $a statistical disclosure control
653 $a microaggregation
655 4 $a Electronic books $2 local. $3 344515
700 1 $a Sa?nchez, David $c (Computer scientist), $e author $3 713989
700 1 $a Soria-Comas, Jordi, $e author $3 713990
776 0 8 $i Print version: $z 9781627058438
830 0 $a Synthesis lectures on information security, privacy and trust ; $v #15. $x 1945-9742 $3 713991
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