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检索条件"任意字段=International Conference on Privacy in Statistical Databases"

共 299 条记录，以下是1-10 订阅

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international conference on privacy in statistical databases, PSD 2024

International Conference on Privacy in Statistical Databases...

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international conference on privacy in statistical databases, PSD 2024

ISBN: (纸本)9783031696503

The proceedings contain 28 papers. The special focus in this conference is on privacy in statistical databases. The topics include: Utility Analysis of Differentially Private Anonymized Data Based on Random Sampling;asymptotic Utility of Spectral Anonymization;robin Hood: A De-identification Method to Preserve Minority Representation for Disparities Research;secondary Cell Suppression by Gaussian Elimination: An Algorithm Suitable for Handling Issues with Zeros and Singletons;obtaining (ϵ,δ)-Differential privacy Guarantees When Using a Poisson Mechanism to Synthesize Contingency Tables;generating Synthetic Data is Complicated: Know Your Data and Know Your Generator;evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling;the Production of Bespoke Synthetic Teaching Datasets Without Access to the Original Data;a Comparison of SynDiffix Multi-table Versus Single-table Synthetic Data;an Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity;evaluation of Synthetic Data Generators on Complex Tabular Data;an Examination of the Alleged privacy Threats of Confidence-Ranked Reconstruction of Census Microdata;synthetic Data: Comparing Utility and Risk in Microdata and Tables;synthetic Data Outliers: Navigating Identity Disclosure;privacy Risk from Synthetic Data: Practical Proposals;attribute Disclosure Risk in Smart Meter Data;the statbarn: A New Model for Output statistical Disclosure Control;masking Georeferenced Health Data - An Analysis Taking the Example of Partially Synthetic Data on Sleep Disorder;privacy and Disclosure Risks in Spatial Dynamic Microsimulations;Combinations of AI Models and XAI Metrics Vulnerable to Record Reconstruction Risk;DISCOLEAF: Personalized DIScretization of COntinuous Attributes for LEArning with Federated Decision Trees;node Injection Link Stealing Attack;Assessing the Potentials of LLMs and GANs as State-of-the-Art Tabular Synthetic Data Generation Methods;escalation of Com

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international conference on privacy in statistical databases, PSD 2022

International Conference on Privacy in Statistical Databases...

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international conference on privacy in statistical databases, PSD 2022

ISBN: (纸本)9783031139444

The proceedings contain 25 papers. The special focus in this conference is on privacy in statistical databases. The topics include: Secure and Non-interactive k-NN Classifier Using Symmetric Fully Homomorphic Encryption;automatic Evaluation of Disclosure Risks of Text Anonymization Methods;generation of Synthetic Trajectory Microdata from Language Models;synthetic Individual Income Tax Data: Methodology, Utility, and privacy Implications;on Integrating the Number of Synthetic Data Sets m into the a priori Synthesis Approach;challenges in Measuring Utility for Fully Synthetic Data;comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata;utility and Disclosure Risk for Differentially Private Synthetic Categorical Data;membership Inference Attack Against Principal Component Analysis;privacy Analysis with a Distributed Transition System and a Data-Wise Metric;when Machine Learning Models Leak: An Exploration of Synthetic Training Data;A Note on the Misinterpretation of the US Census Re-identification Attack;a Re-examination of the Census Bureau Reconstruction and Reidentification Attack;Quality Assessment of the 2014 to 2019 National Survey on Drug Use and Health (NSDUH) Public Use Files;privacy in Practice: Latest Achievements of the Eustat SDC Group;How Adversarial Assumptions Influence Re-identification Risk Measures: A COVID-19 Case Study;multivariate Mean Comparison Under Differential privacy;asking the Proper Question: Adjusting Queries to statistical Procedures Under Differential privacy;towards Integrally Private Clustering: Overlapping Clusters for High privacy Guarantees;perspectives for Tabular Data Protection – How About Synthetic Data?;on privacy of Multidimensional Data Against Aggregate Knowledge Attacks;the Risk of Disclosure When Reporting Commonly Used Univariate Statistics.

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international conference on privacy in statistical databases, PSD 2020

International Conference on Privacy in Statistical Databases...

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international conference on privacy in statistical databases, PSD 2020

ISBN: (纸本)9783030575205

The proceedings contain 25 papers. The special focus in this conference is on privacy in statistical databases. The topics include: Calculation of Risk Probabilities for the Cell Key Method;On Different Formulations of a Continuous CTA Model;privacy Analysis of Query-Set-Size Control;statistical Disclosure Control When Publishing on Thematic Maps;bayesian Modeling for Simultaneous Regression and Record Linkage;probabilistic Blocking and Distributed Bayesian Entity Resolution;secure Matrix Computation: A Viable Alternative to Record Linkage?;a Synthetic Supplemental Public Use File of Low-Income Information Return Data: Methodology, Utility, and privacy Implications;integrating Differential privacy in the statistical Disclosure Control Tool-Kit for Synthetic Data Production;ϵ -Differential privacy for Microdata Releases Does Not Guarantee Confidentiality (Let Alone Utility);advantages of Imputation vs. Data Swapping for statistical Disclosure Control;Evaluating Quality of statistical Disclosure Control Methods – VIOLAS Framework;detecting Bad Answers in Survey Data Through Unsupervised Machine Learning;private Posterior Inference Consistent with Public Information: A Case Study inSmall Area Estimation from Synthetic Census Data;differential privacy and Its Applicability for Official Statistics in Japan – A Comparative Study Using Small Area Data from the Japanese Population Census;disclosure Avoidance in the Census Bureaus 2010 Demonstration Data Product;a Bayesian Nonparametric Approach to Differentially Private Data;a Partitioned Recoding Scheme for privacy Preserving Data Publishing;explaining Recurrent Machine Learning Models: Integral privacy Revisited;utility-Enhancing Flexible Mechanisms for Differential privacy;plausible Deniability;Analysis of Differentially-Private Microdata Using SIMEX;an Analysis of Different Notions ofEffectiveness in k-Anonymity.

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international conference on privacy in statistical databases, PSD 2018

International Conference on Privacy in Statistical Databases...

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international conference on privacy in statistical databases, PSD 2018

ISBN: (纸本)9783319997704

The proceedings contain 23 papers. The special focus in this conference is on privacy in statistical databases. The topics include: The application of genetic algorithms to data synthesis: A comparison of three crossover methods;multiparty computation with statistical input confidentiality via randomized response;Grouping of variables to facilitate SDL methods in multivariate data sets;comparative study of the effectiveness of perturbative methods for creating official microdata in Japan;a general framework and metrics for longitudinal data anonymization;reviewing the methods of estimating the density function based on masked data;protecting values close to zero under the multiplicative noise method;efficiency and sample size determination of protected data;quantifying the protection level of a noise candidate for noise multiplication masking scheme;bounded small cell adjustments for flexible frequency table generators;generalized bayesian record linkage and regression with exact error propagation;probabilistic blocking with an application to the syrian conflict;swapMob: Swapping trajectories for mobility anonymization;safely plotting continuous variables on a map;designing confidentiality on the fly methodology – three aspects;protecting census 2021 origin-destination data using a combination of cell-key perturbation and suppression;on the privacy guarantees of synthetic data: A reassessment from the maximum-knowledge attacker perspective;the quasi-multinomial synthesizer for categorical data;synthetic data via quantile regression for heavy-tailed and heteroskedastic data;some clarifications regarding fully synthetic data;differential correct attribution probability for synthetic data: An exploration.

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international conference on privacy in statistical databases, PSD 2016

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international conference on privacy in statistical databases, PSD 2016

ISBN: (纸本)9783319453804

The proceedings contain 19 papers. The special focus in this conference is on Tabular Data Protection, Microdata, Big Data Masking, Protection Using privacy Models and Synthetic Data. The topics include: An alternative framework of sensitivity measures;empirical analysis of sensitivity rules;cells with frequency exceeding 10 that should be suppressed based on descriptive statistics;a second order cone formulation of continuous CTA model;anonymization in the time of big data;propensity score based conditional group swapping for disclosure limitation of strata-defining variables;a rule-based approach to local anonymization for exclusivity handling in statistical databases;perturbative data protection of multivariate nominal datasets;spatial smoothing and statistical disclosure control;on-average KL-privacy and its equivalence to generalization for max-entropy mechanisms;synthetic microdata from the perspective of distribution type;a synthetic data generator for testing anonymization techniques;towards a national remote access system for register-based research;accurate estimation of structural equation models with remote partitioned data;a new algorithm for protecting aggregate business microdata via a remote system;rank-based record linkage for re-identification risk assessment;computational issues in the design of transition probabilities and disclosure risk estimation for additive noise and enabling collaborative privacy in user-generated emergency reports.

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international conference on privacy in statistical databases, PSD 2014

International Conference on Privacy in Statistical Databases...

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international conference on privacy in statistical databases, PSD 2014

ISBN: (纸本)9783319112565

The proceedings contain 28 papers. The special focus in this conference is on Tabular data protection, Microdata masking, Protection using privacy models, Synthetic data, Record linkage, Remote access and privacy-Preserving protocols. The topics include: Enabling statistical analysis of suppressed tabular data;assessing the information loss of controlled adjustment methods in two-way tables;further developments with perturbation techniques to protect tabular data;comparison of different sensitivity rules for tabular data and presenting a new rule;pre-tabular perturbation with controlled tabular adjustment: some considerations;measuring disclosure risk with entropy in population based frequency tables;A CTA model based on the huber function;density approximant based on noise multiplied data;reverse mapping to preserve the marginal distributions of attributes in masked microdata;JPEG-Based microdata protection;improving the utility of differential privacy via univariate microaggregation;differentially private exponential random graphs;differentially-private logistic regression for detecting Multiple-SNP association in GWAS databases;disclosure risk evaluation for fully synthetic categorical data;v-dispersed synthetic data based on a mixture model with constraints;nonparametric generation of synthetic data for small geographic areas;using partially synthetic data to replace suppression in the business dynamics statistics;synthetic longitudinal business databases for international comparisons;a comparison of blocking methods for record linkage;probabilistic record linkage for disclosure risk assessment;comparison of two remote access systems recently developed and implemented in Australia and towards secure and practical location privacy through private equality testing.

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Obtaining (ε, δ)-Differential privacy Guarantees When Using a Poisson Mechanism to Synthesize Contingency Tables

Obtaining (ε, δ)-Differential Privacy Guarantees When Usin...

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international conference on privacy in statistical databases (PSD)

作者： Jackson, James Mitra, Robin Francis, Brian Dove, Iain Univ Lancaster Lancaster England UCL Dept Stat Sci London England Off Natl Stat Titchfield England

ISBN: (纸本)9783031696503;9783031696510

We show that differential privacy type guarantees can be obtained when using a Poisson synthesis mechanism to protect counts in contingency tables. Specifically, we show how to obtain (epsilon, delta)-probabilistic differential privacy guarantees via the Poisson distribution's cumulative distribution function. We demonstrate this empirically with the synthesis of an administrative-type confidential database.

关键词： contingency tables differential privacy Poisson mechanism synthetic data

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privacy Risk from Synthetic Data: Practical Proposals 1

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international conference on privacy in statistical databases (PSD)

作者： Raab, Gillian M. Univ Edinburgh Scottish Ctr Adm Data Res Edinburgh Midlothian Scotland

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

This paper proposes and compares measures of identity and attribute disclosure risk for synthetic data. Data custodians can use the methods proposed here to inform the decision as to whether to release synthetic versions of confidential data. Different measures are evaluated on two data sets. Insight into the measures is obtained by examining the details of the records identified as posing a disclosure risk. This leads to methods to identify, and possibly exclude, apparently risky records where the identification or attribution would be expected by someone with background knowledge of the data. The methods described are available as part of the synthpop package for R.

关键词： Differential privacy

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"SQLSynthGen: Generating Synthetic Data for Healthcare databases" 4

"SQLSynthGen: Generating Synthetic Data for Healthcare Datab...

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4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Raja, Rohit Singh THREAD Research India

ISBN: (纸本)9798331518882

Synthetic data generation is crucial for leveraging machine learning in healthcare without compromising patient privacy. SQLSynthGen (SSG) offers a solution by generating synthetic datasets from relational databases, preserving data structure and integrity. By extracting statistical properties via SQL queries and applying differential privacy, SSG ensures data utility while protecting individual privacy. Transparency is maintained through configurable YAML files, facilitating data auditing and stakeholder trust. Despite its advancements, SSG faces challenges in relational data handling and privacy explainability, necessitating future improvements in these areas to enhance adoption in healthcare and other sensitive domains. © 2025 IEEE.

关键词： Data integrity

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Differentially Private Quantile Regression 1

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international conference on privacy in statistical databases (PSD)

作者： Tran, Tran Reimherr, Matthew Slavkovic, Aleksandra Penn State Univ Dept Stat State Coll PA 16802 USA

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

Quantile regression (QR) is a powerful and robust statistical modeling method broadly used in many fields such as economics, ecology, and healthcare. However, it has not been well-explored in differential privacy (DP) since its loss function lacks strong convexity and twice differentiability, often required by many DP mechanisms. We implement the smoothed QR loss via convolution within the K-Norm Gradient mechanism (KNG) and prove the resulting estimate converges to the non-private one asymptotically. Additionally, our work is the first to extensively investigate the empirical performance of DP smoothing QR under pure-, approximate- and concentrated-DP and four mechanisms, and cases commonly encountered in practice such as heavy-tailed and heteroscedastic data. We find that the Objective Perturbation Mechanism and KNG are the top performers across the simulated settings.

关键词： Differential privacy Quantile regression Smoothed loss

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