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Co-utility [[electronic resource] ] : Theory and Applications / / edited by Josep Domingo-Ferrer, David Sánchez
| Co-utility [[electronic resource] ] : Theory and Applications / / edited by Josep Domingo-Ferrer, David Sánchez |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (216 pages) : illustrations |
| Disciplina | 153.90151 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Computational intelligence
Game theory Computational Intelligence Game Theory |
| ISBN | 3-319-60234-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Co-Utility: Designing Self-Enforcing and Mutually Beneficial Protocols -- On the Different Forms of Individual and Group Strategic Behavior,and Their Impact on Efficiency -- Co-Utile P2P Anonymous Keyword Search -- Co-Utile Enforcement of Digital Oblivion -- Self-Enforcing Collaborative Anonymization Via Co-Utility -- Aspects of Coalitions for Environmental Protection under Co-utility. |
| Record Nr. | UNINA-9910299902103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inference Control in Statistical Databases [[electronic resource] ] : From Theory to Practice / / edited by Josep Domingo-Ferrer
| Inference Control in Statistical Databases [[electronic resource] ] : From Theory to Practice / / edited by Josep Domingo-Ferrer |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 |
| Descrizione fisica | 1 online resource (VIII, 231 p.) |
| Disciplina | 005.8 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer security
Data encryption (Computer science) Mathematical statistics Database management Computers and civilization Artificial intelligence Systems and Data Security Cryptology Probability and Statistics in Computer Science Database Management Computers and Society Artificial Intelligence |
| ISBN | 3-540-47804-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Advances in Inference Control in Statistical Databases: An Overview -- Advances in Inference Control in Statistical Databases: An Overview -- Tabular Data Protection -- Cell Suppression: Experience and Theory -- Bounds on Entries in 3-Dimensional Contingency Tables Subject to Given Marginal Totals -- Extending Cell Suppression to Protect Tabular Data against Several Attackers -- Network Flows Heuristics for Complementary Cell Suppression: An Empirical Evaluation and Extensions -- HiTaS: A Heuristic Approach to Cell Suppression in Hierarchical Tables -- Microdata Protection -- Model Based Disclosure Protection -- Microdata Protection through Noise Addition -- Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique -- Integrating File and Record Level Disclosure Risk Assessment -- Disclosure Risk Assessment in Perturbative Microdata Protection -- LHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection -- Post-Masking Optimization of the Tradeoff between Information Loss and Disclosure Risk in Masked Microdata Sets -- Software and User Case Studies -- The CASC Project -- Tools and Strategies to Protect Multiple Tables with the GHQUAR Cell Suppression Engine -- SDC in the 2000 U.S. Decennial Census -- Applications of Statistical Disclosure Control at Statistics Netherlands -- Empirical Evidences on Protecting Population Uniqueness at Idescat. |
| Record Nr. | UNINA-9910143909903321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inference Control in Statistical Databases [[electronic resource] ] : From Theory to Practice / / edited by Josep Domingo-Ferrer
| Inference Control in Statistical Databases [[electronic resource] ] : From Theory to Practice / / edited by Josep Domingo-Ferrer |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 |
| Descrizione fisica | 1 online resource (VIII, 231 p.) |
| Disciplina | 005.8 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer security
Data encryption (Computer science) Mathematical statistics Database management Computers and civilization Artificial intelligence Systems and Data Security Cryptology Probability and Statistics in Computer Science Database Management Computers and Society Artificial Intelligence |
| ISBN | 3-540-47804-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Advances in Inference Control in Statistical Databases: An Overview -- Advances in Inference Control in Statistical Databases: An Overview -- Tabular Data Protection -- Cell Suppression: Experience and Theory -- Bounds on Entries in 3-Dimensional Contingency Tables Subject to Given Marginal Totals -- Extending Cell Suppression to Protect Tabular Data against Several Attackers -- Network Flows Heuristics for Complementary Cell Suppression: An Empirical Evaluation and Extensions -- HiTaS: A Heuristic Approach to Cell Suppression in Hierarchical Tables -- Microdata Protection -- Model Based Disclosure Protection -- Microdata Protection through Noise Addition -- Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique -- Integrating File and Record Level Disclosure Risk Assessment -- Disclosure Risk Assessment in Perturbative Microdata Protection -- LHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection -- Post-Masking Optimization of the Tradeoff between Information Loss and Disclosure Risk in Masked Microdata Sets -- Software and User Case Studies -- The CASC Project -- Tools and Strategies to Protect Multiple Tables with the GHQUAR Cell Suppression Engine -- SDC in the 2000 U.S. Decennial Census -- Applications of Statistical Disclosure Control at Statistics Netherlands -- Empirical Evidences on Protecting Population Uniqueness at Idescat. |
| Record Nr. | UNISA-996465409303316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modeling Decisions for Artificial Intelligence [[electronic resource] ] : Third International Conference, MDAI 2006, Tarragona, Spain, April 3-5, 2006, Proceedings / / edited by Vincenc Torra, Yasuo Narukawa, Aïda Valls, Josep Domingo-Ferrer
| Modeling Decisions for Artificial Intelligence [[electronic resource] ] : Third International Conference, MDAI 2006, Tarragona, Spain, April 3-5, 2006, Proceedings / / edited by Vincenc Torra, Yasuo Narukawa, Aïda Valls, Josep Domingo-Ferrer |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
| Descrizione fisica | 1 online resource (XII, 374 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Mathematical logic Computers Database management Computer simulation Operations research Decision making Artificial Intelligence Mathematical Logic and Formal Languages Computation by Abstract Devices Database Management Simulation and Modeling Operations Research/Decision Theory |
| ISBN | 3-540-32781-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- Asymmetric and Compound Preference Aggregators -- Computational Models of Language Toward Brain-Style Computing -- Dominance-Based Rough Set Approach to Case-Based Reasoning -- Towards the Next Generation of Computational Trust and Reputation Models -- Regular Papers -- Preference Modeling by Rectangular Bilattices -- Strategies to Manage Ignorance Situations in Multiperson Decision Making Problems -- An Agent Negotiation Engine for Collaborative Decision Making -- Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach -- The Pairwise Comparison Model: The Multiplicative and the Additive Approach -- Simultaneous Decision Networks with Multiple Objectives as Support for Strategic Planning -- Evaluating Model Construction Methods with Objective Rule Evaluation Indices to Support Human Experts -- A Multicriteria Fuzzy Decision System to Sort Contaminated Soils -- A Comparing Method of Two Team Behaviours in the Simulation Coach Competition -- On the Use of Tools Based on Fuzzy Set Theories in Parametric Software Cost Estimation -- Using Fuzzy Set Theory to Assess Country-of-Origin Effects on the Formation of Product Attitude -- Non-monotonic Fuzzy Measures and Intuitionistic Fuzzy Sets -- A Defuzzification Method of Fuzzy Numbers Induced from Weighted Aggregation Operations -- Dependent OWA Operators -- Decision Aggregation in an Agent-Based Financial Investment Planning System -- Generated Universal Fuzzy Measures -- Aggregation of Valued Relations Applied to Association Rule Interestingness Measures -- On the Relationship Between the Quantifier Threshold and OWA Operators -- Integrated Fuzzy Approach for System Modeling and Risk Assessment -- Watermarking Non-numerical Databases -- New Approach to the Re-identification Problem Using Neural Networks -- Bayesian Correction for SNP Ascertainment Bias -- An Application of Support Vector Machine to Companies’ Financial Distress Prediction -- Probabilistic Verification of Uncertain Systems Using Bounded-Parameter Markov Decision Processes -- Modality Argumentation Programming -- Feature Selection in SVM Based on the Hybrid of Enhanced Genetic Algorithm and Mutual Information -- Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection -- Path Bitmap Indexing for Retrieval of XML Documents -- A Modified Fuzzy C-Means Algorithm for Differentiation in MRI of Ophthalmology -- On Fuzzy c-Means for Data with Tolerance -- On the Use of Variable-Size Fuzzy Clustering for Classification. |
| Record Nr. | UNISA-996466111503316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modeling Decisions for Artificial Intelligence [[electronic resource] ] : Third International Conference, MDAI 2006, Tarragona, Spain, April 3-5, 2006, Proceedings / / edited by Vincenc Torra, Yasuo Narukawa, Aïda Valls, Josep Domingo-Ferrer
| Modeling Decisions for Artificial Intelligence [[electronic resource] ] : Third International Conference, MDAI 2006, Tarragona, Spain, April 3-5, 2006, Proceedings / / edited by Vincenc Torra, Yasuo Narukawa, Aïda Valls, Josep Domingo-Ferrer |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
| Descrizione fisica | 1 online resource (XII, 374 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Mathematical logic Computers Database management Computer simulation Operations research Decision making Artificial Intelligence Mathematical Logic and Formal Languages Computation by Abstract Devices Database Management Simulation and Modeling Operations Research/Decision Theory |
| ISBN | 3-540-32781-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- Asymmetric and Compound Preference Aggregators -- Computational Models of Language Toward Brain-Style Computing -- Dominance-Based Rough Set Approach to Case-Based Reasoning -- Towards the Next Generation of Computational Trust and Reputation Models -- Regular Papers -- Preference Modeling by Rectangular Bilattices -- Strategies to Manage Ignorance Situations in Multiperson Decision Making Problems -- An Agent Negotiation Engine for Collaborative Decision Making -- Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach -- The Pairwise Comparison Model: The Multiplicative and the Additive Approach -- Simultaneous Decision Networks with Multiple Objectives as Support for Strategic Planning -- Evaluating Model Construction Methods with Objective Rule Evaluation Indices to Support Human Experts -- A Multicriteria Fuzzy Decision System to Sort Contaminated Soils -- A Comparing Method of Two Team Behaviours in the Simulation Coach Competition -- On the Use of Tools Based on Fuzzy Set Theories in Parametric Software Cost Estimation -- Using Fuzzy Set Theory to Assess Country-of-Origin Effects on the Formation of Product Attitude -- Non-monotonic Fuzzy Measures and Intuitionistic Fuzzy Sets -- A Defuzzification Method of Fuzzy Numbers Induced from Weighted Aggregation Operations -- Dependent OWA Operators -- Decision Aggregation in an Agent-Based Financial Investment Planning System -- Generated Universal Fuzzy Measures -- Aggregation of Valued Relations Applied to Association Rule Interestingness Measures -- On the Relationship Between the Quantifier Threshold and OWA Operators -- Integrated Fuzzy Approach for System Modeling and Risk Assessment -- Watermarking Non-numerical Databases -- New Approach to the Re-identification Problem Using Neural Networks -- Bayesian Correction for SNP Ascertainment Bias -- An Application of Support Vector Machine to Companies’ Financial Distress Prediction -- Probabilistic Verification of Uncertain Systems Using Bounded-Parameter Markov Decision Processes -- Modality Argumentation Programming -- Feature Selection in SVM Based on the Hybrid of Enhanced Genetic Algorithm and Mutual Information -- Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection -- Path Bitmap Indexing for Retrieval of XML Documents -- A Modified Fuzzy C-Means Algorithm for Differentiation in MRI of Ophthalmology -- On Fuzzy c-Means for Data with Tolerance -- On the Use of Variable-Size Fuzzy Clustering for Classification. |
| Record Nr. | UNINA-9910767504003321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Privacy in statistical databases : International Conference, PSD 2022, Paris, France, September 21-23, 2022, proceedings / / Josep Domingo-Ferrer and Maryline Laurent, editors
| Privacy in statistical databases : International Conference, PSD 2022, Paris, France, September 21-23, 2022, proceedings / / Josep Domingo-Ferrer and Maryline Laurent, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022] |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 005.8 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Data protection
Database security |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-13945-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Privacy Models -- An Optimization-Based Decomposition Heuristic for the Microaggregation Problem -- 1 Introduction -- 2 The Decomposition Heuristic -- 3 The Local Search Improvement Heuristic -- 4 The Mixed Integer Linear Optimization Algorithm Based on Column Generation -- 5 Computational Results -- 6 Conclusions -- References -- Privacy Analysis with a Distributed Transition System and a Data-Wise Metric -- 1 Introduction -- 2 Distributed Labeled-Tagged Transition Systems -- 3 -Indistinguishability, -Local-Differential Privacy -- 4 -Differential Privacy -- 5 Comparing Two Nodes on One or More Runs -- 6 New Metric for Indistinguishability and DP -- 7 Related Work and Conclusion -- References -- Multivariate Mean Comparison Under Differential Privacy -- 1 Introduction -- 2 Mathematical Background -- 2.1 Statistical Tests for Two Samples -- 2.2 Hotelling's t2-Test -- 2.3 Differential Privacy -- 3 Privatized Mean Comparison -- 3.1 Privatization of the t2-Statistic -- 3.2 Bootstrap -- 4 Simulation -- 5 Conclusion -- A Proofs -- B Effects of Privatization - Example -- C Algorithms -- References -- Asking the Proper Question: Adjusting Queries to Statistical Procedures Under Differential Privacy -- 1 Introduction -- 1.1 Setting -- 1.2 Our Contribution -- 1.3 Related Work -- 2 Fixed (Non-random) Datasets -- 2.1 Confidence Regions -- 2.2 Testing Hypotheses: Likelihood-Ratio Test -- 3 Random, Normally Distributed Data -- 3.1 Confidence Regions -- 3.2 Testing Hypotheses: Likelihood-Ratio Test -- 4 Numerical Example -- 5 Appendix -- References -- Towards Integrally Private Clustering: Overlapping Clusters for High Privacy Guarantees -- 1 Introduction -- 2 Preliminaries -- 2.1 Integral Privacy -- 2.2 k-Anonymity, Microaggregation, and MDAV -- 2.3 Genetic Algorithms -- 3 -Centroid c-Means.
3.1 Formalization -- 3.2 Properties -- 4 Experiments -- 4.1 Solving the Optimization Problem -- 4.2 Datasets -- 4.3 Parameters -- 4.4 Results -- 5 Conclusions -- References -- II Tabular Data -- Perspectives for Tabular Data Protection - How About Synthetic Data? -- 1 Introduction -- 2 Recalling the Methods under Consideration -- 2.1 Synthetic Data -- 2.2 Targeted Record Swapping (TRS) -- 2.3 CKM Noise Design for Tabulations of Continuous Variables -- 3 Study Design -- 3.1 Test Data -- 3.2 Application Settings for Synthetic Data Generation -- 3.3 Application Settings for Targeted Record Swapping -- 3.4 Application Settings for the Cell Key Method -- 4 Measuring Utility and Disclosure Risk -- 5 Results -- 5.1 Comparing Utility Loss -- 6 Summary, Open Issues, Conclusions -- Appendix -- A.1 Approximate Behavior of Utility Loss Indicator U for CKM in Extremely Detailed Tabulations -- Appendix A.2 -- Appendix A.3 -- References -- On Privacy of Multidimensional Data Against Aggregate Knowledge Attacks -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 3.1 Preliminaries -- 3.2 Problem Definition -- 4 Privacy-Preserving Method -- 4.1 Preprocessing Step -- 4.2 Space Allocation Step -- 4.3 View Creation Step -- 5 Experimental Evaluation -- 6 Discussion -- 7 Conclusion -- References -- Synthetic Decimal Numbers as a Flexible Tool for Suppression of Post-published Tabular Data -- 1 Introduction -- 2 A Motivating Example -- 3 A Theoretical Framework -- 4 Application of the Theory -- 4.1 Application to Frequency Tables -- 4.2 Precision and Implementation -- 4.3 Application to Magnitude Tables -- 4.4 Detection of Disclosure Risk -- 5 Real Applications -- 5.1 Register-Based Employment Statistics -- 5.2 Commissioned Data in Business Statistics -- 6 Concluding Remarks -- References -- Disclosure Risk Assessment and Record Linkage. The Risk of Disclosure When Reporting Commonly Used Univariate Statistics -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Discussion Example -- 5 Graphical Representation -- 6 Conclusion -- Appendix -- References -- Privacy-Preserving Protocols -- Tit-for-Tat Disclosure of a Binding Sequence of User Analyses in Safe Data Access Centers -- 1 Introduction -- 2 Background -- 3 Binding to a Sequence of Analyses with Tit-for-Tat Analysis Disclosure -- 3.1 Instruction Preparation -- 3.2 Hash-Based Coding and Execution -- 3.3 Security Properties -- 4 Using Binding Sequences of User Analyses to Ensure Ethical Compliance -- 4.1 Checking Confidentiality -- 4.2 Ex ante Checking of Explainability and Fairness -- 5 Conclusions and Further Research -- References -- Secure and Non-interactive k-NN Classifier Using Symmetric Fully Homomorphic Encryption -- 1 Introduction -- 2 Background -- 2.1 Homomorphic Encryption -- 2.2 Functional Bootstrap in TFHE -- 3 Our Contribution -- 3.1 The System Model -- 3.2 Encrypted k-NN Challenges -- 3.3 Our Proposed k-NN Algorithm -- 4 Performance Evaluation -- 4.1 Test Environment -- 4.2 Performance Results -- 5 Conclusion -- References -- Unstructured and Mobility Data -- Automatic Evaluation of Disclosure Risks of Text Anonymization Methods -- 1 Introduction -- 2 Background -- 3 A Re-identification Attack for Evaluating Anonymized Text -- 3.1 Building the Classifier -- 4 Empirical Experiments -- 4.1 Results -- 5 Conclusions and Future Work -- References -- Generation of Synthetic Trajectory Microdata from Language Models -- 1 Introduction -- 2 Related Work -- 2.1 Sequential Models for Trajectory Prediction -- 2.2 Privacy-Preserving Tajectory Data Publishing -- 3 Synthetic Trajectory Generation Method -- 3.1 Data Preprocessing -- 3.2 Next-Point Prediction Model -- 3.3 Synthetic Data Generation -- 4 Experimental Analysis. 4.1 Data Sets and Preprocessing -- 4.2 Model Training -- 4.3 Results of Data Generation -- 4.4 Additional Remarks on the Experimental Work -- 5 Conclusions and Future Work -- References -- Synthetic Data -- Synthetic Individual Income Tax Data: Methodology, Utility, and Privacy Implications -- 1 Introduction -- 2 Data Synthesis Methodology -- 2.1 Administrative Tax Data -- 2.2 Synthetic Data Generation -- 2.3 Disclosure Risk Measures -- 3 Evaluation -- 4 Conclusions and Future Work -- References -- On Integrating the Number of Synthetic Data Sets m into the a priori Synthesis Approach -- 1 Introduction -- 2 Review of the Use of Saturated Models for Synthesis -- 2.1 The Metrics -- 3 The Role of m as a Tuning Parameter -- 3.1 Obtaining Inferences from m> -- 1 Data Sets -- 4 Introducing the 3(k,d) and 4(k,d) Metrics -- 5 Empirical Study -- 5.1 Measuring Risk -- 5.2 Measuring Utility -- 5.3 Tuning m and in Relation to the Risk-Utility Trade-Off -- 6 Discussion -- References -- Challenges in Measuring Utility for Fully Synthetic Data -- 1 Introduction -- 2 Measuring the Utility -- 2.1 A Global Utility Measure: The pMSE -- 2.2 Two Outcome-Specific Measure: The Confidence Interval Overlap and the Mean Absolute Standardized Coefficient Difference -- 3 Misleading Utility Measures: An Illustration -- 3.1 Synthesis Strategies -- 3.2 Results for the Fit-for-Purpose Measures -- 3.3 Results for the Outcome Specific Measures -- 3.4 Results for the Global Utility Measures -- 4 Conclusions -- References -- Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata -- 1 Introduction -- 2 Background -- 2.1 Data Synthesis -- 2.2 Synthetic Census Microdata -- 3 Research Design -- 3.1 Data Synthesisers -- 3.2 Data -- 3.3 Measuring Disclosure Risk Using TCAP -- 3.4 Evaluating Utility -- 4 Results -- 5 Discussion -- 6 Conclusion -- References. Utility and Disclosure Risk for Differentially Private Synthetic Categorical Data -- 1 Introduction -- 2 Methods for Creating Synthetic Data Sets -- 2.1 Without DP Guarantee -- 2.2 Adapting Methods for Synthetic Data to Make Them DP -- 3 Measures of Utility and Disclosure Risk for Synthetic Categorical Data -- 3.1 Disclosure Risk -- 3.2 Utility -- 4 Data Sets Used for the Evaluation -- 5 Results -- 5.1 Utility and Disclosure Risk for Non-DP Synthesis -- 5.2 Utility and Disclosure Risk for DP Synthesis -- 6 Discussion and Future Work -- A Appendix -- A.1 Details of the Variables in Data Sets -- References -- Machine Learning and Privacy -- Membership Inference Attack Against Principal Component Analysis -- 1 Introduction -- 2 Background -- 2.1 Principal Component Analysis -- 2.2 Membership Inference Attacks -- 3 Related Work -- 4 Membership Inference Attacks Against PCA -- 4.1 Threat Model and Attack Methodology -- 4.2 Experimental Setup -- 4.3 Experimental Results -- 5 Differentially-Private PCA and MIA -- 5.1 Preliminaries on Differential Privacy -- 5.2 Differentially Private PCA Approaches -- 5.3 Experimental Results -- 6 Conclusion -- References -- When Machine Learning Models Leak: An Exploration of Synthetic Training Data -- 1 Introduction -- 2 Threat Model -- 3 Background and Related Work -- 3.1 Propensity to Move -- 3.2 Privacy in Machine Learning -- 3.3 Attribute Inference Attack -- 4 Experimental Setup -- 4.1 Data Set -- 4.2 Utility Measures -- 4.3 Adversary Resources -- 5 Experimental Results -- 5.1 Evaluation of Machine Learning Algorithms -- 5.2 Model Inversion Attribute Inference Attack -- 6 Conclusion and Future Work -- References -- Case Studies -- A Note on the Misinterpretation of the US Census Re-identification Attack -- 1 Introduction -- 2 The Bureau's Re-identification Attack -- 3 Our Simple Inference Non-attack. 3.1 Effect of Majority Race/Ethnicity Threshold. |
| Record Nr. | UNINA-9910595049203321 |
| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Privacy in statistical databases : International Conference, PSD 2022, Paris, France, September 21-23, 2022, proceedings / / Josep Domingo-Ferrer and Maryline Laurent, editors
| Privacy in statistical databases : International Conference, PSD 2022, Paris, France, September 21-23, 2022, proceedings / / Josep Domingo-Ferrer and Maryline Laurent, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022] |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 005.8 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Data protection
Database security |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-13945-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Privacy Models -- An Optimization-Based Decomposition Heuristic for the Microaggregation Problem -- 1 Introduction -- 2 The Decomposition Heuristic -- 3 The Local Search Improvement Heuristic -- 4 The Mixed Integer Linear Optimization Algorithm Based on Column Generation -- 5 Computational Results -- 6 Conclusions -- References -- Privacy Analysis with a Distributed Transition System and a Data-Wise Metric -- 1 Introduction -- 2 Distributed Labeled-Tagged Transition Systems -- 3 -Indistinguishability, -Local-Differential Privacy -- 4 -Differential Privacy -- 5 Comparing Two Nodes on One or More Runs -- 6 New Metric for Indistinguishability and DP -- 7 Related Work and Conclusion -- References -- Multivariate Mean Comparison Under Differential Privacy -- 1 Introduction -- 2 Mathematical Background -- 2.1 Statistical Tests for Two Samples -- 2.2 Hotelling's t2-Test -- 2.3 Differential Privacy -- 3 Privatized Mean Comparison -- 3.1 Privatization of the t2-Statistic -- 3.2 Bootstrap -- 4 Simulation -- 5 Conclusion -- A Proofs -- B Effects of Privatization - Example -- C Algorithms -- References -- Asking the Proper Question: Adjusting Queries to Statistical Procedures Under Differential Privacy -- 1 Introduction -- 1.1 Setting -- 1.2 Our Contribution -- 1.3 Related Work -- 2 Fixed (Non-random) Datasets -- 2.1 Confidence Regions -- 2.2 Testing Hypotheses: Likelihood-Ratio Test -- 3 Random, Normally Distributed Data -- 3.1 Confidence Regions -- 3.2 Testing Hypotheses: Likelihood-Ratio Test -- 4 Numerical Example -- 5 Appendix -- References -- Towards Integrally Private Clustering: Overlapping Clusters for High Privacy Guarantees -- 1 Introduction -- 2 Preliminaries -- 2.1 Integral Privacy -- 2.2 k-Anonymity, Microaggregation, and MDAV -- 2.3 Genetic Algorithms -- 3 -Centroid c-Means.
3.1 Formalization -- 3.2 Properties -- 4 Experiments -- 4.1 Solving the Optimization Problem -- 4.2 Datasets -- 4.3 Parameters -- 4.4 Results -- 5 Conclusions -- References -- II Tabular Data -- Perspectives for Tabular Data Protection - How About Synthetic Data? -- 1 Introduction -- 2 Recalling the Methods under Consideration -- 2.1 Synthetic Data -- 2.2 Targeted Record Swapping (TRS) -- 2.3 CKM Noise Design for Tabulations of Continuous Variables -- 3 Study Design -- 3.1 Test Data -- 3.2 Application Settings for Synthetic Data Generation -- 3.3 Application Settings for Targeted Record Swapping -- 3.4 Application Settings for the Cell Key Method -- 4 Measuring Utility and Disclosure Risk -- 5 Results -- 5.1 Comparing Utility Loss -- 6 Summary, Open Issues, Conclusions -- Appendix -- A.1 Approximate Behavior of Utility Loss Indicator U for CKM in Extremely Detailed Tabulations -- Appendix A.2 -- Appendix A.3 -- References -- On Privacy of Multidimensional Data Against Aggregate Knowledge Attacks -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 3.1 Preliminaries -- 3.2 Problem Definition -- 4 Privacy-Preserving Method -- 4.1 Preprocessing Step -- 4.2 Space Allocation Step -- 4.3 View Creation Step -- 5 Experimental Evaluation -- 6 Discussion -- 7 Conclusion -- References -- Synthetic Decimal Numbers as a Flexible Tool for Suppression of Post-published Tabular Data -- 1 Introduction -- 2 A Motivating Example -- 3 A Theoretical Framework -- 4 Application of the Theory -- 4.1 Application to Frequency Tables -- 4.2 Precision and Implementation -- 4.3 Application to Magnitude Tables -- 4.4 Detection of Disclosure Risk -- 5 Real Applications -- 5.1 Register-Based Employment Statistics -- 5.2 Commissioned Data in Business Statistics -- 6 Concluding Remarks -- References -- Disclosure Risk Assessment and Record Linkage. The Risk of Disclosure When Reporting Commonly Used Univariate Statistics -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Discussion Example -- 5 Graphical Representation -- 6 Conclusion -- Appendix -- References -- Privacy-Preserving Protocols -- Tit-for-Tat Disclosure of a Binding Sequence of User Analyses in Safe Data Access Centers -- 1 Introduction -- 2 Background -- 3 Binding to a Sequence of Analyses with Tit-for-Tat Analysis Disclosure -- 3.1 Instruction Preparation -- 3.2 Hash-Based Coding and Execution -- 3.3 Security Properties -- 4 Using Binding Sequences of User Analyses to Ensure Ethical Compliance -- 4.1 Checking Confidentiality -- 4.2 Ex ante Checking of Explainability and Fairness -- 5 Conclusions and Further Research -- References -- Secure and Non-interactive k-NN Classifier Using Symmetric Fully Homomorphic Encryption -- 1 Introduction -- 2 Background -- 2.1 Homomorphic Encryption -- 2.2 Functional Bootstrap in TFHE -- 3 Our Contribution -- 3.1 The System Model -- 3.2 Encrypted k-NN Challenges -- 3.3 Our Proposed k-NN Algorithm -- 4 Performance Evaluation -- 4.1 Test Environment -- 4.2 Performance Results -- 5 Conclusion -- References -- Unstructured and Mobility Data -- Automatic Evaluation of Disclosure Risks of Text Anonymization Methods -- 1 Introduction -- 2 Background -- 3 A Re-identification Attack for Evaluating Anonymized Text -- 3.1 Building the Classifier -- 4 Empirical Experiments -- 4.1 Results -- 5 Conclusions and Future Work -- References -- Generation of Synthetic Trajectory Microdata from Language Models -- 1 Introduction -- 2 Related Work -- 2.1 Sequential Models for Trajectory Prediction -- 2.2 Privacy-Preserving Tajectory Data Publishing -- 3 Synthetic Trajectory Generation Method -- 3.1 Data Preprocessing -- 3.2 Next-Point Prediction Model -- 3.3 Synthetic Data Generation -- 4 Experimental Analysis. 4.1 Data Sets and Preprocessing -- 4.2 Model Training -- 4.3 Results of Data Generation -- 4.4 Additional Remarks on the Experimental Work -- 5 Conclusions and Future Work -- References -- Synthetic Data -- Synthetic Individual Income Tax Data: Methodology, Utility, and Privacy Implications -- 1 Introduction -- 2 Data Synthesis Methodology -- 2.1 Administrative Tax Data -- 2.2 Synthetic Data Generation -- 2.3 Disclosure Risk Measures -- 3 Evaluation -- 4 Conclusions and Future Work -- References -- On Integrating the Number of Synthetic Data Sets m into the a priori Synthesis Approach -- 1 Introduction -- 2 Review of the Use of Saturated Models for Synthesis -- 2.1 The Metrics -- 3 The Role of m as a Tuning Parameter -- 3.1 Obtaining Inferences from m> -- 1 Data Sets -- 4 Introducing the 3(k,d) and 4(k,d) Metrics -- 5 Empirical Study -- 5.1 Measuring Risk -- 5.2 Measuring Utility -- 5.3 Tuning m and in Relation to the Risk-Utility Trade-Off -- 6 Discussion -- References -- Challenges in Measuring Utility for Fully Synthetic Data -- 1 Introduction -- 2 Measuring the Utility -- 2.1 A Global Utility Measure: The pMSE -- 2.2 Two Outcome-Specific Measure: The Confidence Interval Overlap and the Mean Absolute Standardized Coefficient Difference -- 3 Misleading Utility Measures: An Illustration -- 3.1 Synthesis Strategies -- 3.2 Results for the Fit-for-Purpose Measures -- 3.3 Results for the Outcome Specific Measures -- 3.4 Results for the Global Utility Measures -- 4 Conclusions -- References -- Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata -- 1 Introduction -- 2 Background -- 2.1 Data Synthesis -- 2.2 Synthetic Census Microdata -- 3 Research Design -- 3.1 Data Synthesisers -- 3.2 Data -- 3.3 Measuring Disclosure Risk Using TCAP -- 3.4 Evaluating Utility -- 4 Results -- 5 Discussion -- 6 Conclusion -- References. Utility and Disclosure Risk for Differentially Private Synthetic Categorical Data -- 1 Introduction -- 2 Methods for Creating Synthetic Data Sets -- 2.1 Without DP Guarantee -- 2.2 Adapting Methods for Synthetic Data to Make Them DP -- 3 Measures of Utility and Disclosure Risk for Synthetic Categorical Data -- 3.1 Disclosure Risk -- 3.2 Utility -- 4 Data Sets Used for the Evaluation -- 5 Results -- 5.1 Utility and Disclosure Risk for Non-DP Synthesis -- 5.2 Utility and Disclosure Risk for DP Synthesis -- 6 Discussion and Future Work -- A Appendix -- A.1 Details of the Variables in Data Sets -- References -- Machine Learning and Privacy -- Membership Inference Attack Against Principal Component Analysis -- 1 Introduction -- 2 Background -- 2.1 Principal Component Analysis -- 2.2 Membership Inference Attacks -- 3 Related Work -- 4 Membership Inference Attacks Against PCA -- 4.1 Threat Model and Attack Methodology -- 4.2 Experimental Setup -- 4.3 Experimental Results -- 5 Differentially-Private PCA and MIA -- 5.1 Preliminaries on Differential Privacy -- 5.2 Differentially Private PCA Approaches -- 5.3 Experimental Results -- 6 Conclusion -- References -- When Machine Learning Models Leak: An Exploration of Synthetic Training Data -- 1 Introduction -- 2 Threat Model -- 3 Background and Related Work -- 3.1 Propensity to Move -- 3.2 Privacy in Machine Learning -- 3.3 Attribute Inference Attack -- 4 Experimental Setup -- 4.1 Data Set -- 4.2 Utility Measures -- 4.3 Adversary Resources -- 5 Experimental Results -- 5.1 Evaluation of Machine Learning Algorithms -- 5.2 Model Inversion Attribute Inference Attack -- 6 Conclusion and Future Work -- References -- Case Studies -- A Note on the Misinterpretation of the US Census Re-identification Attack -- 1 Introduction -- 2 The Bureau's Re-identification Attack -- 3 Our Simple Inference Non-attack. 3.1 Effect of Majority Race/Ethnicity Threshold. |
| Record Nr. | UNISA-996490367603316 |
| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Privacy in statistical databases : UNESCO Chair in Data Privacy, International Conference, PSD 2020, Tarragona, Spain, September 23-25, 2020 : proceedings / / Josep Domingo-Ferrer, Krishnamurty Muralidhar (editors)
| Privacy in statistical databases : UNESCO Chair in Data Privacy, International Conference, PSD 2020, Tarragona, Spain, September 23-25, 2020 : proceedings / / Josep Domingo-Ferrer, Krishnamurty Muralidhar (editors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XI, 370 p. 25 illus.) |
| Disciplina | 005.8 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database security
Data protection Statistics - Databases |
| ISBN | 3-030-57521-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Privacy models -- Microdata protection -- Protection of statistical tables -- Protection of interactive and mobility databases -- Record linkage and alternative methods -- Synthetic data -- Data quality -- Case studies. |
| Record Nr. | UNINA-9910427714303321 |
| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Privacy in statistical databases : UNESCO Chair in Data Privacy, International Conference, PSD 2020, Tarragona, Spain, September 23-25, 2020 : proceedings / / Josep Domingo-Ferrer, Krishnamurty Muralidhar (editors)
| Privacy in statistical databases : UNESCO Chair in Data Privacy, International Conference, PSD 2020, Tarragona, Spain, September 23-25, 2020 : proceedings / / Josep Domingo-Ferrer, Krishnamurty Muralidhar (editors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XI, 370 p. 25 illus.) |
| Disciplina | 005.8 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database security
Data protection Statistics - Databases |
| ISBN | 3-030-57521-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Privacy models -- Microdata protection -- Protection of statistical tables -- Protection of interactive and mobility databases -- Record linkage and alternative methods -- Synthetic data -- Data quality -- Case studies. |
| Record Nr. | UNISA-996418295203316 |
| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Privacy in Statistical Databases [[electronic resource] ] : UNESCO Chair in Data Privacy, International Conference, PSD 2018, Valencia, Spain, September 26–28, 2018, Proceedings / / edited by Josep Domingo-Ferrer, Francisco Montes
| Privacy in Statistical Databases [[electronic resource] ] : UNESCO Chair in Data Privacy, International Conference, PSD 2018, Valencia, Spain, September 26–28, 2018, Proceedings / / edited by Josep Domingo-Ferrer, Francisco Montes |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XI, 361 p. 78 illus.) |
| Disciplina | 006.312 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Data mining
Information storage and retrieval Computers and civilization Computer security Mathematical statistics Artificial intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Computers and Society Systems and Data Security Probability and Statistics in Computer Science Artificial Intelligence |
| ISBN | 3-319-99771-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Tabular Data Protection -- Synthetic Data -- Microdata and Big Data Masking -- Record Linkage -- Spatial and Mobility Data. |
| Record Nr. | UNINA-9910349407503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
