Privacy in Statistical Databases: UNESCO Chair in Data Privacy International Conference, PSD 2010 Corfu, Greece, September 22-24, 2010 Proceedings

Cover......Page 1
Lecture Notes in Computer Science 6344......Page 2
Privacy in Statistical Databases: UNESCO Chair in Data Privacy International Conference, PSD 2010 Corfu, Greece, September 22-24, 2010 Proceedings......Page 3
Preface......Page 5
Table of Contents......Page 9
Introduction......Page 12
Related Work......Page 13
Our Research Scenario and Solution......Page 14
Revisit of Privacy Disclosure Patterns......Page 15
Modeling of Error Distribution of Marginal Values......Page 16
Estimation of Privacy Disclosure Probability......Page 17
Control of Privacy Disclosure......Page 18
Marginal Value Vibration......Page 19
Experiments......Page 20
Control of Privacy Disclosure......Page 21
Experimental Conclusion and Finding......Page 22
References......Page 23
Introduction......Page 28
The RCTA Problem......Page 29
The Elastic Programming Approach for Analyzing Infeasibility......Page 31
The Infeasibility Repair Tool......Page 33
Example......Page 34
Computational Results......Page 36
References......Page 37
Introduction......Page 40
Background and Notation......Page 41
Branch-and-Cut Algorithm for Cell Suppression......Page 43
Cut-and-Branch Algorithm for Cell Suppression......Page 46
Computational Results......Page 47
An Exact Algorithm for Protecting Counting Tables......Page 50
References......Page 51
Introduction......Page 52
Targeted Data Swap Strategy......Page 54
Data......Page 56
Disclosure Risk......Page 57
Data Utility......Page 59
R-U Confidentiality Map......Page 61
References......Page 62
Introduction......Page 63
Methodological Background of SAFE......Page 64
How to Create Zero-Mean / Fixed Variance Cell Perturbations?......Page 65
How to Combine Invariance and a “No-Small-Cells” Requirement?......Page 67
Selection of Random Noise......Page 69
How to Restore Table-Additivity?......Page 70
Summary and Final Remarks......Page 72
References......Page 73
The Set of SBS Tables......Page 77
The Naive Way......Page 79
The Traditional Way......Page 80
Conclusions......Page 82
References......Page 83
Introduction......Page 85
Thwarting Intruders......Page 87
Statistical Disclosure Controls......Page 88
An Evaluation of Security......Page 93
Conclusion......Page 94
References......Page 95
Introduction......Page 96
Definitions and Problem Description......Page 98
Trajectory Poly-Lines and Two-Dimensional Surfaces......Page 99
Duality Transformation of Boundary-Trajectories and Information Distortion......Page 100
The Proposed Algorithm for Privacy-Aware Indexing......Page 101
Experimental Evaluation......Page 103
Query Cost Comparison......Page 105
References......Page 106
Introduction......Page 108
PRAM Matrices......Page 109
Outline of Evolutionary Algorithms......Page 110
Genetic Operators......Page 111
Fitness Function......Page 112
Experimental Results......Page 113
Conclusions......Page 116
References......Page 117
Introduction......Page 118
Multiplicative Noise Protocols......Page 120
Preservation of Inequality Constraints......Page 124
Numerical Experiments......Page 125
References......Page 128
Introduction......Page 129
Adding Noise to Continuous Variables......Page 130
Misclassification of Categorical Variables......Page 133
Discussion......Page 135
References......Page 137
Introduction......Page 138
Microaggregation......Page 139
Motivations of Our Proposal......Page 140
ODP Classification......Page 141
Partition......Page 142
Aggregation......Page 143
Usefulness Evaluation Method......Page 144
Related Work......Page 146
References......Page 147
Introduction......Page 149
The Data Environment Analysis......Page 150
Data Environment Analysis Methods......Page 151
The Key Variable Mapping System (KVMS)......Page 154
Discussion......Page 155
References......Page 156
Introduction......Page 159
Support Vector Machines......Page 161
Illustrative Simulation......Page 163
The IAB Establishment Panel......Page 165
Data Utility Evaluation......Page 166
Disclosure Risk Evaluation......Page 168
Conclusions......Page 170
References......Page 171
Introduction......Page 173
Review of Fully Synthetic Data......Page 175
Stage 1: Direct Estimates......Page 176
Stage 2: Sampling Distribution and Between-Area Model......Page 177
Stage 3: Generating Synthetic Populations within Small Areas......Page 178
Evaluation of Synthetic Data for Small Area Inferences......Page 179
Univariate Inferences for Small Areas......Page 180
Multivariate Inferences for Small Areas......Page 181
Conclusions......Page 182
References......Page 183
Introduction......Page 185
Generation of Synthetic Population Data......Page 186
Synthetic EU-SILC Population Data......Page 188
A Global Disclosure Risk Measure for Survey Data......Page 189
Confidentiality of Synthetic Population Data......Page 190
Scenario 1: Attack Using One-to-One Matches in Key Variables with Information on the Data Generation Process......Page 192
Scenario 4: Attack Using All Occurrences in Key Variables without Information on the Data Generation Process......Page 193
Results......Page 194
Conclusions......Page 195
References......Page 196
Introduction......Page 198
Notation for Binary Contingency Tables......Page 199
The Differential Privacy Mechanism for Contingency Tables......Page 200
Empirical Evaluation of the Differential Privacy Mechanism......Page 202
References......Page 207
Introduction......Page 211
“Terry Gross is Two Inches Shorter Than the Average Lithuanian Woman”......Page 212
“Mr. Overy Rich’s Income Is $5 Million More Than the Average American”......Page 216
Conclusions......Page 219
References......Page 220
Implementing a Differential Privacy Based Procedure......Page 221
Laplace Noise Addition to Satisfy Differential Privacy......Page 222
Vulnerability to Tracker Attack......Page 224
Conclusions......Page 229
References......Page 230
Introduction......Page 231
The Formal Approach......Page 233
The IAB Establishment Panel......Page 235
Empirical Evidence......Page 236
Conclusion......Page 239
References......Page 240
Introduction......Page 245
Who Will Use the MAS and Will It Cost Anything?......Page 246
Confidentiality Rules for Universe Formation......Page 247
Confidentiality Rules for Regression Models......Page 251
Evaluation of the Effectiveness of the Drop Q Rule......Page 252
References......Page 255
Introduction......Page 260
Overview......Page 261
Client Workstation......Page 262
Database and File Systems......Page 263
Eurostat Efforts......Page 264
Way Forward at Eurostat......Page 265
References......Page 267
Introduction......Page 269
Contribution and Plan of This Paper......Page 270
Coprivacy and Its Generalizations......Page 271
Coprivacy in P2P User-Private Information Retrieval......Page 273
Correlated Coprivacy in Social Networks......Page 274
Conclusions and Research Directions......Page 277
References......Page 278
Introduction......Page 280
Problem Definition......Page 281
Parameter Estimation......Page 282
Blocking......Page 283
Secure Multiparty Computation......Page 284
Alternative Security Models......Page 285
Privacy Preserving Record Linkage......Page 286
Database Joins and Set Intersection......Page 287
Record Pair Similarity......Page 288
Blocking......Page 290
Prominent Unsolved Challenges......Page 291
References......Page 292
Introduction......Page 295
European Anonymisation Process: Structural Constraints and Different Situations......Page 296
Analysis of the Current Anonymisation Flow and Its Critical Points......Page 297
Proposal for a Harmonised European Anonymisation......Page 299
Working on the Input of the Process: Statistical Methodology......Page 300
Working on the Output of the Process: Comparable Dissemination......Page 301
Release of Multiple Types of Files......Page 303
Conclusions......Page 304
References......Page 305
Author Index......Page 308