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Author Domingo-Ferrer, Josep., author
Title Database anonymization : privacy models, data utility, and microaggregation-based inter-model connections / Josep Domingo-Ferrer, David Sánchez, and Jordi Soria-Comas
Imprint San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2016
book jacket
Descript 1 online resource(xv, 120 pages) : illustrations
text rdacontent
electronic isbdmedia
online resource rdacarrier
Series Synthesis lectures on information security, privacy, and trust, 1945-9750 ; # 15
Synthesis digital library of engineering and computer science
Synthesis lectures on information security, privacy, and trust ; # 15. 1945-9750
Note Part of: Synthesis digital library of engineering and computer science
Includes bibliographical references (pages 109-118)
1. Introduction --
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 --
3. Anonymization methods for microdata -- 3.1 Non-perturbative masking methods -- 3.2 Perturbative masking methods -- 3.3 Synthetic data generation -- 3.4 Summary --
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 --
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 --
6. Beyond k-anonymity: l-diversity and t -closeness -- 6.1 l-diversity -- 6.2 t-closeness -- 6.3 Summary --
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 --
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 --
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 --
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 --
11. Conclusions and research directions -- 11.1 Summary and conclusions -- 11.2 Research directions -- Bibliography -- Authors' biographies
Abstract freely available; full-text restricted to subscribers or individual document purchasers
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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. 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
Also available in print
Title from PDF title page (viewed on January 22, 2016)
Link Print version: 9781627058438
Subject Data protection
Database security
data releases
privacy protection
privacy models
statistical disclosure limitation
statistical disclosure control
Alt Author Sánchez, David., author
Soria-Comas, Jordi., author
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