Verfügbarkeit: Data privacy: foundations, new developments and the big data challenge

Data privacy: foundations, new developments and the big data challenge:

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Beteilige Person:	Torra, Vicenç (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	Englisch
Veröffentlicht:	Cham Springer [2017]
Schriftenreihe:	Studies in Big Data 28
Schlagwörter:	Engineering Artificial intelligence Computational intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Data Mining Privatsphäre Datenschutz Datensicherung
Links:	https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 https://doi.org/10.1007/978-3-319-57358-8 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029738059&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	1 Online-Ressource (xiv, 269 Seiten) Illustrationen
ISBN:	9783319573588
DOI:	10.1007/978-3-319-57358-8

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adam_text	Titel: Data privacy: foundations, new developments and the big data challenge Autor: Torra, Vicenç Jahr: 2017 Contents 1 Introduction.............................................. 1 1.1 Motivations for Data Privacy............................. 2 1.2 Privacy and Society.................................... 5 1.3 Terminology.......................................... 6 1.3.1 The Framework.................................. 7 1.3.2 Anonymity and Unlinkability....................... 8 1.3.3 Disclosure...................................... 10 1.3.4 Undetectability and Unobservability.................. 13 1.3.5 Pseudonyms and Identity.......................... 14 1.3.6 Transparency.................................... 16 1.4 Privacy and Disclosure.................................. 17 1.5 Privacy by Design..................................... 17 References................................................ 19 2 Machine and Statistical Learning............................. 23 2.1 Classification of Techniques.............................. 24 2.2 Supervised Learning.................................... 25 2.2.1 Classification.................................... 25 2.2.2 Regression...................................... 26 2.2.3 Validation of Results: k-Fold Cross-Validation.......... 27 2.3 Unsupervised Learning.................................. 28 2.3.1 Clustering...................................... 28 2.3.2 Association Rules................................ 44 2.3.3 Expectation-Maximization Algorithm................. 51 References................................................ 53 3 On the Classification of Protection Procedures.................. 55 3.1 Dimensions........................................... 55 3.1.1 On Whose Privacy Is Being Sought.................. 56 3.1.2 On the Computations to be Done.................... 58 3.1.3 On the Number of Data Sources..................... 60 3.1.4 Knowledge Intensive Data Privacy................... 61 3.1.5 Other Dimensions and Discussion.................... 62 3.1.6 Summary....................................... 63 _________________________________________________________________Contents 3.2 Respondent and Holder Privacy........................... 64 3.3 Data-Driven Methods................................... 65 3.4 Computation-Driven Methods............................. 66 3.4.1 Single Database: Differential Privacy................. 66 3.4.2 Multiple Databases: Cryptographic Approaches......... 69 3.4.3 Discussion...................................... 70 3.5 Result-Driven Approaches............................... 71 3.6 Tabular Data.......................................... 76 3.6.1 Cell Suppression................................. 80 3.6.2 Controlled Tabular Adjustment...................... 83 References................................................ 85 User s Privacy............................................ 89 4.1 User Privacy in Communications.......................... 90 4.1.1 Protecting the Identity of the User................... 90 4.1.2 Protecting the Data of the User...................... 95 4.2 User Privacy in Information Retrieval...................... 97 4.2.1 Protecting the Identity of the User................... 97 4.2.2 Protecting the Query of the User..................... 98 4.3 Private Information Retrieval............................. 101 4.3.1 Information-Theoretic PIR with k Databases............ 101 4.3.2 Computational PIR............................... 104 4.3.3 Other Contexts.................................. 108 References................................................ 108 Privacy Models and Disclosure Risk Measures.................. Ill 5.1 Definition and Controversies.............................. Ill 5.1.1 A Boolean or Measurable Condition.................. 114 5.2 Attribute Disclosure.................................... 115 5.2.1 Attribute Disclosure for a Numerical Variable.......... 115 5.2.2 Attribute Disclosure for a Categorical Variable.......... 116 5.3 Identity Disclosure..................................... 118 5.3.1 An Scenario for Identity Disclosure.................. 118 5.3.2 Measures for Identity Disclosure..................... 121 5.3.3 Uniqueness..................................... 122 5.3.4 Reidentification.................................. 123 5.3.5 The Worst-Case Scenario.......................... 125 5.4 Matching and Integration: A Database Based Approach......... 126 5.4.1 Heterogenous Distributed Databases.................. 127 5.4.2 Data Integration................................. 127 5.4.3 Schema Matching................................ 131 5.4.4 Data Matching.................................. 133 5.4.5 Preprocessing................................... 134 5.4.6 Indexing and Blocking............................ 135 Contents_______________________________________._______ xiii 5.4.7 Record Pair Comparison: Distances and Similarities...... 136 5.4.8 Classification of Record Pairs....................... 140 5.5 Probabilistic Record Linkage............................. 142 5.5.1 Alternative Expressions for Decision Rules............. 152 5.5.2 Computation of Rp(a, b)........................... 156 5.5.3 Estimation of the Probabilities...................... 158 5.5.4 Extensions for Computing Probabilities............... 160 5.5.5 Final Notes..................................... 163 5.6 Distance-Based Record Linkage........................... 163 5.6.1 Weighted Distances............................... 164 5.6.2 Distance and Normalization........................ 167 5.6.3 Parameter Determination for Record Linkage........... 168 5.7 Record Linkage Without Common Variables................. 171 5.8 ^-Anonymity and Other Boolean Conditions for Identity Disclosure.................................. 172 5.8.1 fc-Anonymity and Anonymity Sets: A-Confusion......... 173 5.8.2 ^-Anonymity and Attribute Disclosure: Attacks......... 176 5.8.3 Other Related Approaches to fc-Anonymity............. 178 5.9 Discussion on Record Linkage............................ 178 5.9.1 Formalization of Reidentification Algorithms........... 178 5.9.2 Comparison of Record Linkage Algorithms............ 179 5.9.3 Disclosure Risk and Big Data....................... 180 5.9.4 Some Guidelines and Research Issues................. 182 References................................................ 183 6 Masking Methods......................................... 191 6.1 Perturbative Methods................................... 193 6.1.1 Data and Rank Swapping.......................... 193 6.1.2 Microaggregation................................ 200 6.1.3 Additive and Multiplicative Noise.................... 212 6.1.4 PRAM......................................... 214 6.1.5 Lossy Compression and Other Transform-Based Method........................................ 217 6.2 Non-perturbative Methods............................... 219 6.2.1 Generalization and Recoding........................ 219 6.2.2 Suppression..................................... 220 6.3 Synthetic Data Generators............................... 220 6.4 Masking Methods and ^-anonymity........................ 223 6.4.1 Mondrian...................................... 224 6.4.2 Algorithms for fc-anonymity: Variants and Big Data...... 225 6.5 Data Protection Procedures for Constrained Data.............. 226 6.5.1 Types of Constraints.............................. 227 6.6 Masking Methods and Big Data........................... 230 References................................................ 231 x]v_______________________________________________________________ Contents 7 Information Loss: Evaluation and Measures.................... 239 7.1 Generic Versus Specific Information Loss................... 239 7.2 Information Loss Measures............................... 240 7.3 Generic Information Loss Measures........................ 242 7.3.1 Numerical Data.................................. 242 7.3.2 Categorical Data................................. 245 7.4 Specific Information Loss................................ 248 7.4.1 Classification-Based Information Loss................. 248 7.4.2 Regression-Based Information Loss.................. 249 7.4.3 Clustering-Based Information Loss................... 249 7.5 Information Loss and Big Data............................ 250 References................................................ 251 8 Selection of Masking Methods............................... 255 8.1 Aggregation: A Score................................... 255 8.2 Visualization: R-U Maps................................ 256 8.3 Optimization and Postmasking............................ 257 References................................................ 258 9 Conclusions.............................................. 259 Reference................................................ 260 Index...................................................... 261
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id	DE-604.BV044334880
illustrated	Illustrated
indexdate	2024-12-20T18:00:12Z
institution	BVB
isbn	9783319573588
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-029738059
oclc_num	989821988
open_access_boolean
owner	DE-859 DE-860 DE-91 DE-BY-TUM DE-1046 DE-1043 DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-573 DE-863 DE-BY-FWS DE-706 DE-634 DE-862 DE-BY-FWS DE-92 DE-83
owner_facet	DE-859 DE-860 DE-91 DE-BY-TUM DE-1046 DE-1043 DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-573 DE-863 DE-BY-FWS DE-706 DE-634 DE-862 DE-BY-FWS DE-92 DE-83
physical	1 Online-Ressource (xiv, 269 Seiten) Illustrationen
psigel	ZDB-2-ENG ZDB-2-ENG_2017
publishDate	2017
publishDateSearch	2017
publishDateSort	2017
publisher	Springer
record_format	marc
series	Studies in Big Data
series2	Studies in Big Data
spellingShingle	Torra, Vicenç Data privacy: foundations, new developments and the big data challenge Studies in Big Data Engineering Artificial intelligence Computational intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Data Mining (DE-588)4428654-5 gnd Privatsphäre (DE-588)4123980-5 gnd Datenschutz (DE-588)4011134-9 gnd Datensicherung (DE-588)4011144-1 gnd
subject_GND	(DE-588)4428654-5 (DE-588)4123980-5 (DE-588)4011134-9 (DE-588)4011144-1
title	Data privacy: foundations, new developments and the big data challenge
title_auth	Data privacy: foundations, new developments and the big data challenge
title_exact_search	Data privacy: foundations, new developments and the big data challenge
title_full	Data privacy: foundations, new developments and the big data challenge Vicenç Torra
title_fullStr	Data privacy: foundations, new developments and the big data challenge Vicenç Torra
title_full_unstemmed	Data privacy: foundations, new developments and the big data challenge Vicenç Torra
title_short	Data privacy: foundations, new developments and the big data challenge
title_sort	data privacy foundations new developments and the big data challenge
topic	Engineering Artificial intelligence Computational intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Data Mining (DE-588)4428654-5 gnd Privatsphäre (DE-588)4123980-5 gnd Datenschutz (DE-588)4011134-9 gnd Datensicherung (DE-588)4011144-1 gnd
topic_facet	Engineering Artificial intelligence Computational intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Data Mining Privatsphäre Datenschutz Datensicherung
url	https://doi.org/10.1007/978-3-319-57358-8 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029738059&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV041583423
work_keys_str_mv	AT torravicenc dataprivacyfoundationsnewdevelopmentsandthebigdatachallenge

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