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检索条件"任意字段=International Conference on Privacy in Statistical Databases, PSD 2010"

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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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Escalation of Commitment: A Case Study of the United States Census Bureau Efforts to Implement Differential privacy for the 2020 Decennial Census 1

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international conference on privacy in statistical databases (psd)

作者： Muralidhar, Krishnamurty Ruggles, Steven Univ Oklahoma Norman OK 73019 USA Univ Minnesota Minneapolis MN 55455 USA IPUMS Minneapolis MN 55455 USA

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

In 2017, the United States Census Bureau announced that because of high disclosure risk in the methodology (data swapping) used to produce tabular data for the 2010 census, a different protection mechanism based on differential privacy would be used for the 2020 census. While there have been many studies evaluating the result of this change, there has been no rigorous examination of disclosure risk claims resulting from the released 2010 tabular data. In this study we perform such an evaluation. We show that the procedures used to evaluate disclosure risk are unreliable and resulted in inflated disclosure risk. Demonstration data products released using the new procedure were also shown to have poor utility. However, since the Census Bureau had already committed to a different procedure, they had no option except to escalate their commitment. The result of such escalation is that the 2020 tabular data release offers neither privacy nor accuracy.

关键词： Census disclosure privacy

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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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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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From Isolation to Identification 1

引用

international conference on privacy in statistical databases (psd)

作者： D'Acquisto, Giuseppe Cohen, Aloni Naldi, Maurizio Nissim, Kobbi LUISS Univ Rome Italy Univ Chicago Chicago IL 60637 USA LUMSA Univ Rome Italy Georgetown Univ Washington DC USA

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

We present a mathematical framework for understanding when successfully distinguishing a person from all other persons in a data set-a phenomenon which we call isolation-may enable identification, a notion which is central to deciding whether a release based on the data set is subject to data protection regulation. We show that a baseline degree of isolation is unavoidable in the sense that isolation can typically happen with high probability even before a release was made about the data set and hence identification is not enabled. We then describe settings where isolation resulting from a data release may enable identification.

关键词： privacy identification isolation data protection

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Synthetic Data Outliers: Navigating Identity Disclosure 1

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international conference on privacy in statistical databases (psd)

作者： Trindade, Carolina Antunes, Luis Carvalho, Tania Moniz, Nuno Univ Porto Fac Ciencias Porto Portugal TekPrivacy Porto Portugal Lucy Family Inst Data & Soc Notre Dame IN USA

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

Multiple synthetic data generation models have emerged, among which deep learning models have become the vanguard due to their ability to capture the underlying characteristics of the original data. However, the resemblance of the synthetic to the original data raises important questions on the protection of individuals' privacy. As synthetic data is perceived as a means to fully protect personal information, most current related work disregards the impact of re-identification risk. In particular, limited attention has been given to exploring outliers, despite their privacy relevance. In this work, we analyze the privacy of synthetic data w.r.t the outliers. Our main findings suggest that outliers re-identification via linkage attack is feasible and easily achieved. Furthermore, additional safeguards such as differential privacy can prevent re-identification, albeit at the expense of the data utility.

关键词： Synthetic Data Outliers Deep Learning Differential privacy Data privacy Data Utility

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Node Injection Link Stealing Attack 1

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international conference on privacy in statistical databases (psd)

作者： Zari, Oualid Parra-Arnau, Javier Unsal, Ayse Onen, Melek EURECOM Biot France Karlsruhe Inst Technol Karlsruhe Germany Univ Politecn Cataluna Barcelona Spain

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

We present a stealthy privacy attack that exposes links in Graph Neural Networks (GNNs). Focusing on dynamic GNNs, we propose to inject new nodes and attach them to a particular target node to infer its private edge information. Our approach significantly enhances the F-1 score of the attack compared to the current state-of-the-art bench-marks. Specifically, for the Twitch dataset, our method improves the F-1 score by 23.75%, and for the Flickr dataset, remarkably, it is more than three times better than the state-of-the-art. We also propose and evaluate defense strategies based on differentially private (DP) mechanisms relying on a newly defined DP notion. These solutions, on average, reduce the effectiveness of the attack by 71.9% while only incurring a minimal utility loss of about 3.2%.

关键词： graph neural networks link stealing differential privacy

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