Data privacy: foundations, new developments and the big data challenge
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer
[2017]
|
| Schriftenreihe: | Studies in Big Data
Volume 28 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030273749&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xiv, 269 Seiten Illustrationen, Diagramme |
| ISBN: | 9783319573564 |
| ISSN: | 2197-6503 |
Internformat
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| 650 | 4 | |a Computational intelligence | |
| 650 | 4 | |a Computational Intelligence | |
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Datensatz im Suchindex
| _version_ | 1819314090525327360 |
|---|---|
| adam_text | 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 Unîinkability............................... 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
64
65
66
66
69
70
71
76
80
83
85
89
90
90
95
97
97
98
101
101
104
108
108
111
111
114
115
115
116
118
118
121
122
123
125
126
127
127
131
133
134
135
3.2 Respondent and Holder Privacy.......................
3.3 Data-Driven Methods.................................
3.4 Computation-Driven Methods..........................
3.4.1 Single Database: Differential Privacy........
3.4.2 Multiple Databases: Cryptographic Approaches
3.4.3 Discussion...................................
3.5 Result-Driven Approaches............................
3.6 Tabular Data........................................
3.6.1 Cell Suppression.............................
3.6.2 Controlled Tabular Adjustment................
References...............................................
User’s Privacy...........................................
4.1 User Privacy in Communications......................
4.1.1 Protecting the Identity of the User..........
4.1.2 Protecting the Data of the User..............
4.2 User Privacy in Information Retrieval...............
4.2.1 Protecting the Identity of the User..........
4.2.2 Protecting the Query of the User.............
4.3 Private Information Retrieval.......................
4.3.1 Information-Theoretic PIR with k Databases. . .
4.3.2 Computational PIR . .........................
4.3.3 Other Contexts...............................
References...............................................
Privacy Models and Disclosure Risk Measures..............
5.1 Definition and Controversies........................
5.1.1 A Boolean or Measurable Condition............
5.2 Attribute Disclosure................................
5.2.1 Attribute Disclosure for a Numerical Variable .
5.2.2 Attribute Disclosure for a Categorical Variable.
5.3 Identity Disclosure.................................
5.3.1 An Scenario for Identity Disclosure..........
5.3.2 Measures for Identity Disclosure.............
5.3.3 Uniqueness...................................
5.3.4 Reidentification.............................
5.3.5 The Worst-Case Scenario......................
5.4 Matching and Integration: A Database Based Approach
5.4.1 Heterogenous Distributed Databases...........
5.4.2 Data Integration.............................
5.4.3 Schema Matching..............................
5.4.4 Data Matching................................
5.4.5 Preprocessing................................
5.4.6 Indexing and Blocking........................
Contents x^i
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 ¿-Anonymity and Anonymity Sets: ¿-Confusion............. . 173
5.8.2 ¿-Anonymity and Attribute Disclosure: Attacks............ 176
5.8.3 Other Related Approaches to ¿-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 ¿-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
xiv 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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| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Rechtswissenschaft Informatik |
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| id | DE-604.BV044879496 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T18:13:07Z |
| institution | BVB |
| isbn | 9783319573564 |
| issn | 2197-6503 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030273749 |
| oclc_num | 1014391721 |
| open_access_boolean | |
| owner | DE-384 DE-355 DE-BY-UBR |
| owner_facet | DE-384 DE-355 DE-BY-UBR |
| physical | xiv, 269 Seiten Illustrationen, Diagramme |
| 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 Datensicherung (DE-588)4011144-1 gnd Datenschutz (DE-588)4011134-9 gnd Privatsphäre (DE-588)4123980-5 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4011144-1 (DE-588)4011134-9 (DE-588)4123980-5 |
| 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 |
| title_sort | data privacy foundations new developments and the big data challenge |
| title_sub | 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 Datensicherung (DE-588)4011144-1 gnd Datenschutz (DE-588)4011134-9 gnd Privatsphäre (DE-588)4123980-5 gnd |
| topic_facet | Engineering Artificial intelligence Computational intelligence Computational Intelligence Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Data Mining Datensicherung Datenschutz Privatsphäre |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030273749&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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