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共 129 条记录，以下是11-20 订阅

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Synthetic Data: Comparing Utility and Risk in Microdata and Tables 1

引用

international conference on privacy in statistical databases (psd)

作者： Kolb, Simon Xi Ning Tang, Jui Andreas Giessing, Sarah Fed Stat Off Germany D-65180 Wiesbaden Germany

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

Synthetic data has begun to show potential as an alternative to traditional SDC methods in specific use cases. This development and the increasing research efforts further hint at an emerging role in future privacy protection. However, since data synthesis predominantly happens at microdata level, development of utility and risk metrics is also focused on this domain. statistical agencies on the other hand limit data publication mostly to aggregates, by selecting various subsets of variables for cross tabulation. We analyze the correlations between microdata and tabular data metrics for assessing utility and risk. Using a large real life data set as an example for data synthesis, we show that certain global metrics may disproportionately represent small subsets of variables, making them an inappropriate estimator for the quality of aggregates. On the other hand, we show strong similarities between certain microdata level risk metrics and risks of group disclosure in aggregated data.

关键词： Synthetic Data SDC Data privacy pMSE TCAP SUDA

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Secondary Cell Suppression by Gaussian Elimination: An Algorithm Suitable for Handling Issues with Zeros and Singletons 1

引用

international conference on privacy in statistical databases (psd)

作者： Langsrud, Oyvind Stat Norway Postboks 2633 St Hanshaugen N-0131 Oslo Norway

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

To protect tabular data through cell suppression, efficient algorithms are essential. Gaussian elimination can be used for secondary cell suppression to prevent exact disclosure. A beneficial feature of this method is that all tables created from the same microdata can be handled simultaneously. This paper presents a solution to the issue where suppressed zeros in frequency tables cannot protect each other. In magnitude tables, it outlines how the algorithm can be tailored to provide protection against singleton contributors using their own data for disclosure.

关键词： statistical disclosure control Confidentiality Tabular data Cell suppression Official statistics

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Utility Analysis of Differentially Private Anonymized Data Based on Random Sampling 1

引用

international conference on privacy in statistical databases (psd)

作者： Sugiyama, Takumi Oosugi, Hiroto Yamanaka, Io Minami, Kazuhiro Chuo Univ Grad Sch Econ Hachioji Tokyo Japan Raksu Co Ltd Shinagawa Japan SECOM Co Ltd Shibuya Japan Inst Stat Math Dept Interdisciplinary Stat Math Tachikawa Tokyo Japan

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

It is possible to produce differentially private k-anonymized data based on the method of random sampling followed by full-domain generalization for k-anonymization. We previously evaluate the performance of that method, which is implemented as the SafePub algorithm in the ARX anonymization tool. However, since the SafePub algorithm uses the maximum sampling rate that satisfies the requirements for differential privacy, we observe paradoxical results where data utility diminishes as the privacy budget for differential privacy increases. In this paper, we, therefore, conduct preliminary experiments to explore the parameter space for privacy budget and sampling rate by setting the sampling rates explicitly through modifications to the implementation of ARX. Our initial results show the possibility of improving the utility of anonymized data by properly setting the sampling rate below its maximum value.

关键词： Differential privacy Random Sampling k-anonymity

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The statbarn: A New Model for Output statistical Disclosure Control 1

引用

international conference on privacy in statistical databases (psd)

作者： Green, Elizabeth Ritche, Felix White, Paul Univ West England Bristol Coldhabour Lane Bristol BS16 1QY Avon England

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

A major success for research this century has been the growth of secure facilities allowing research access to detailed sensitive personal data. This has also raised awareness of the problem of output disclosure risk, where statistics may inadvertently breach the confidentiality of data subjects, a risk that grows with the detail in the data. Managing this risk is a concern for these secure facilities. While there is a well-established literature on the protection of frequency tables and linear aggregates, researchers in secure facilities produce a wide range of statistical outputs. The theory covering non-tabular outputs is small, fractured, and has grown ad hoc. This is also reflected in the guidance available to data service staff, which typically consists of a long list of outputs and some rules to be applied to them. This paper describes a significant new concept in output statistical disclosure control: the statistical barn or 'statbarn'. This is a framework to classify all statistical terms by their disclosure characteristics, including risk, exceptions and mitigation measures. This statbarn massively reduces the dimensionality of the disclosure checking problem, as well as providing improved clarity. It also creates a feasible basis for automatic disclosure control checking.

关键词： statbarn output checking statistical disclosure control

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Masking Georeferenced Health Data - An Analysis Taking the Example of Partially Synthetic Data on Sleep Disorder 1

引用

international conference on privacy in statistical databases (psd)

作者： Cremer, Simon Jehmlich, Lydia Lenz, Rainer Cologne Univ Technol Arts & Sci Inst Prod D-50679 Cologne Germany Tech Univ Dortmund Dept Stat D-44221 Dortmund Germany

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

Spatial health data is becoming increasingly important in health research. However, the desired information can often not be extracted despite the inherent analytical content. The reason for this is that access to personal georeferenced data sets is severely restricted as they are subject to legal data protection. The method of donut masking attempts to alienate original data by shifting it in such a way that data protection is guaranteed without strongly reducing the analytical validity of the data. In this article donut masking is applied to partially synthetic data on sleep disorders. The degree of anonymity of the masked data set is measured by spatial k-anonymity reviewing additional knowledge of a potential data attacker. In addition to assessing the spatial similarity of the original and masked data set, an attempt is also made to assess the suitability of such data for analysis purposes.

关键词： privacy-enhancing technologies geoprivacy location privacy medical informatics spatial analysis donut masking geographic health spatial k-anonymity

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An Examination of the Alleged privacy Threats of Confidence-Ranked Reconstruction of Census Microdata 1

引用

international conference on privacy in statistical databases (psd)

作者： Sanchez, David Jebreel, Najeeb Muralidhar, Krishnamurty Domingo-Ferrer, Josep Blanco-Justicia, Alberto Univ Rovira & Virgili CYBERCAT Ctr Cybersecur Res Catalonia Dept Comp Sci & Math Ave Pailsos Catalans 26 Tarragona 43007 Catalonia Spain Univ Oklahoma Price Coll Business Dept Mkt & Supply Chain Management 307 West BrooksAdams HallRoom 10 Norman OK 73019 USA

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

The threat of reconstruction attacks has led the U.S. Census Bureau (USCB) to replace in the Decennial Census 2020 the traditional statistical disclosure limitation based on rank swapping with one based on differential privacy (DP), leading to substantial accuracy loss of released statistics. Yet, it has been argued that, if many different reconstructions are compatible with the released statistics, most of them do not correspond to actual original data, which protects against respondent reidentification. Recently, a new attack has been proposed, which incorporates the confidence that a reconstructed record was in the original data. The alleged risk of disclosure entailed by such confidence-ranked reconstruction has renewed the interest of the USCB to use DP-based solutions. To forestall a potential accuracy loss in future releases, we show that the proposed reconstruction is neither effective as a reconstruction method nor conducive to disclosure as claimed by its authors. Specifically, we report empirical results showing the proposed ranking cannot guide reidentification or attribute disclosure attacks, and hence fails to warrant the utility sacrifice entailed by the use of DP to release census statistical data.

关键词： US Decennial Census 2020 statistical Disclosure Limitation Reconstruction Differential privacy Reidentification

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Relational Or Single: A Comparative Analysis of Data Synthesis Approaches for privacy and Utility on a Use Case from statistical Office 1

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international conference on privacy in statistical databases (psd)

作者： Slokom, Manel Agrawal, Shruti Krol, Nynke C. de Wolf, Peter-Paul Stat Netherlands The Hague Netherlands Ctr Wiskunde & Informat Amsterdam Netherlands

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

This paper presents a case study focused on synthesizing relational datasets within Official Statistics for software and technology testing purposes. Specifically, the focus is on generating synthetic data for testing and validating software code. Our study conducts a comprehensive comparative analysis of various synthesis approaches tailored for a multi-table relational database featuring a one-to-one relationship versus a single table. We leverage state-of-the-art single and multi-table synthesis methods to evaluate their potential to maintain the analytical validity of the data, ensure data utility, and mitigate risks associated with disclosure. The evaluation of analytical validity includes assessing how well synthetic data replicates the structure and characteristics of real datasets. First, we compare synthesis methods based on their ability to maintain constraints and conditional dependencies found in real data. Second, we evaluate the utility of synthetic data by training linear regression models on both real and synthetic datasets. Lastly, we measure the privacy risks associated with synthetic data by conducting attribute inference attacks to measure the disclosure risk of sensitive attributes. Our experimental results indicate that the single-table data synthesis method demonstrates superior performance in terms of analytical validity, utility, and privacy preservation compared to the multi-table synthesis method. However, we find promise in the premise of multi-table data synthesis in protecting against attribute disclosure, albeit calling for future exploration to improve the utility of the data.

关键词： Relational data data synthesis inference constraints Single vs Multi

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Asymptotic Utility of Spectral Anonymization 1

引用

international conference on privacy in statistical databases (psd)

作者： Perkonoja, Katariina Virta, Joni Univ Turku Dept Math & Stat Turku Finland Univ Turku Dept Comp Turku Finland

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

In the contemporary data landscape characterized by multi-source data collection and third-party sharing, ensuring individual privacy stands as a critical concern. While various anonymization methods exist, their utility preservation and privacy guarantees remain challenging to quantify. In this work, we address this gap by studying the utility and privacy of the spectral anonymization (SA) algorithm, particularly in an asymptotic framework. Unlike conventional anonymization methods that directly modify the original data, SA operates by perturbing the data in a spectral basis and subsequently reverting them to their original basis. Alongside the original version P-SA, employing random permutation transformation, we introduce two novel SA variants: J-spectral anonymization and O-spectral anonymization, which employ sign-change and orthogonal matrix transformations, respectively. We show how well, under some practical assumptions, these SA algorithms preserve the first and second moments of the original data. Our results reveal, in particular, that the asymptotic efficiency of all three SA algorithms in covariance estimation is exactly 50% when compared to the original data. To assess the applicability of these asymptotic results in practice, we conduct a simulation study with finite data and also evaluate the privacy protection offered by these algorithms using distance-based record linkage. Our research reveals that while no method exhibits clear superiority in finite-sample utility, O-SA distinguishes itself for its exceptional privacy preservation, never producing identical records, albeit with increased computational complexity. Conversely, P-SA emerges as a computationally efficient alternative, demonstrating unmatched efficiency in mean estimation.

关键词： Anonymization Limiting distribution privacy protection Singular value decomposition Utility

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An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity 1

引用

international conference on privacy in statistical databases (psd)

作者： Foessing, Emma Drechsler, Joerg Georg August Univ Gottingen Germany Inst Employment Res Nurnberg Germany Ludwig Maximilians Univ Munchen Munich Germany Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

Generating synthetic data has never been so easy. With the increasing popularity of the approach more and more R packages and Python libraries offer ready-made synthesizers that promise generating synthetic data with almost no effort. These synthetic data generators rely on various modeling strategies, such as generative adversarial networks, Bayesian networks or variational autoencoders. Given the plethora of methods, users new to the approach have an increasingly hard time to decide where to even start when exploring the possibilities of synthetic data. This paper aims at offering some guidance by empirically evaluating the analytical validity of 12 different synthesizers available in the Python library synthcity. While this comparison study offers only a small glimpse into the world of synthetic data (many more synthetic data generators exist and we also only rely on the default settings when training the various models), we still hope the evaluations offer some useful insights regarding the performance of the different synthesis strategies.

关键词： Confidentiality GAN Neural Networks privacy Synthetic

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Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling 1

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international conference on privacy in statistical databases (psd)

作者： Oganian, Anna Drechsler, Jorg Iqbal, Mehtab Ctr Dis Control & Prevent Natl Ctr Hlth Stat 3311 Toledo Rd Hyattsville MD 20782 USA Inst Employment Res Regensburger Str 104 D-90478 Nurnberg Germany Clemson Univ Sch Comp 821 McMillan Rd Clemson SC 29631 USA

ISBN: (数字)9783031696510

ISBN: (纸本)9783031696503;9783031696510

In recent years, national statistical organizations have increasingly relied on synthetic data when releasing microdata containing sensitive personal or establishment information. This paper deals with the challenges of using synthetic data to protect the privacy of survey respondents. For this type of data it is often important to consider the survey design information when creating the synthesis models. The paper discusses two techniques that can be used for generating survey microdata under informative sampling. Specifically, it examines an approach that combines design-based and model-based methods through the use of the pseudo-likelihood approach within the sequential regression framework. As far as we are aware, the pseudo-likelihood method has not been used in the context of sequential regression synthesis before. This method is compared with another approach in which design variables are included as predictors in the regression models. In the latter approach, the survey weights have to be synthesized and included in the final data product, while the former generates synthetic simple random samples that are representative of the original population without weights.

关键词： statistical disclosure limitation survey data pseudo likelihood synthetic data probability proportional to size sampling sequential regression modeling survey weights

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