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international conference on privacy in statistical databases (psd)

作者： Pistner, Michelle Slavkovic, Aleksandra Vilhuber, Lars Penn State Univ Dept Stat University Pk PA 16802 USA Cornell Univ Econ Dept Ithaca NY 14853 USA

ISBN: (纸本)9783319997704;9783319997711

privacy protection of confidential data is a fundamental problem faced by many government organizations and research centers. It is further complicated when data have complex structures or variables with highly skewed distributions. The statistical community addresses general privacy concerns by introducing different techniques that aim to decrease disclosure risk in released data while retaining their statistical properties. However, methods for complex data structures have received insufficient attention. We propose producing synthetic data via quantile regression to address privacy protection of heavy-tailed and heteroskedastic data. We address some shortcomings of the previously proposed use of quantile regression as a synthesis method and extend the work into cases where data have heavy tails or heteroskedastic errors. Using a simulation study and two applications, we show that there are settings where quantile regression performs as well as or better than other commonly used synthesis methods on the basis of maintaining good data utility while simultaneously decreasing disclosure risk.

关键词： Data privacy

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Multiparty Computation with statistical Input Confidentiality via Randomized Response

Multiparty Computation with Statistical Input Confidentialit...

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international conference on privacy in statistical databases (psd)

作者： Domingo-Ferrer, Josep Mulero-Vellido, Rafael Soria-Comas, Jordi Univ Rovira & Virgili CYBERCAT Ctr Cybersecur Res Catalonia Dept Comp Sci & Math UNESCO Chair Data Privacy Av Paisos Catalans 26 Tarragona 43007 Catalonia Spain

ISBN: (纸本)9783319997704;9783319997711

We explore a setting in which a number of subjects want to compute on their pooled data while keeping the statistical confidentiality of their input. statistical confidentiality is different from the cryptographic confidentiality guaranteed by cryptographic multiparty secure computation: whereas in the latter nothing is disclosed about the input, in statistical input confidentiality a noise-added version of the input is disclosed, which allows more flexible computations. We propose a protocol based on local anonymization via randomized response, whereby the empirical distribution of the data of the subjects is approximated. From that distribution, most statistical calculations can be approximated as well. Regarding the accuracy of the approximation, ceteris paribus it improves with the number of subjects. Large dimensionality (that is, a large number of attributes) decreases accuracy and we propose a strategy to mitigate the dimensionality problem. We show how to characterize the privacy guarantee for each subject in terms of differential privacy. Experimental work is reported on the attained accuracy as a function of the number of respondents, number of attributes and randomized response parameters.

关键词： Multiparty anonymous computation Randomized response Local anonymization Big data privacy

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On the privacy Guarantees of Synthetic Data: A Reassessment from the Maximum-Knowledge Attacker Perspective

On the Privacy Guarantees of Synthetic Data: A Reassessment ...

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international conference on privacy in statistical databases (psd)

作者： Ruiz, Nicolas Muralidhar, Krishnamurty Domingo-Ferrer, Josep Univ Rovira & Virgili CYBERCAT Ctr Cybersecur Res Catalonia Dept Comp Sci & Math UNESCO Chair Data Privacy Av Paisos Catalans 26 Tarragona 43007 Catalonia Spain Univ Oklahoma Price Coll Business Dept Mkt & Supply Chain Management 308 Brooks St Norman OK 73019 USA

ISBN: (纸本)9783319997704;9783319997711

Generating synthetic data for the dissemination of individual information in a privacy-preserving way is an approach that is often presented as superior to other statistical disclosure control techniques. The reason for such claim is straightforward at first glance: since all records disseminated are synthetic and not actual observed values, no individual can reasonably claim to face a privacy threat. Thus, and if the synthesizer used is good enough, synthetic data will potentially always offer a high level of information with low disclosure risk attached. Building on recent advances in the literature regarding the conceptualization of an intruder, this paper aims at challenging this claim by reassessing the privacy guarantees of synthetic data. Using the concept of a maximum-knowledge intruder, we demonstrate that synthetic data can in fact be always expressed as a re-arrangement of the original data and that, as a result, they may lead to configurations where disclosure risk may be higher than for nonsynthetic disclosure control approaches. We illustrate the application of these results by an empirical example.

关键词： statistical disclosure control Synthetic data Maximum-knowledge attacker

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pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity

<i>pMSE</i> Mechanism: Differentially Private Synthetic Data...

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international conference on privacy in statistical databases (psd)

作者： Snoke, Joshua Slavkovic, Aleksandra Penn State Univ Dept Stat University Pk PA 16802 USA

ISBN: (纸本)9783319997704;9783319997711

We propose a method for the release of differentially private synthetic datasets. In many contexts, data contain sensitive values which cannot be released in their original form in order to protect individuals' privacy. Synthetic data is a protection method that releases alternative values in place of the original ones, and differential privacy (DP) is a formal guarantee for quantifying the privacy loss. We propose a method that maximizes the distributional similarity of the synthetic data relative to the original data using a measure known as the pMSE, while guaranteeing epsilon-DP. We relax common DP assumptions concerning the distribution and boundedness of the original data. We prove theoretical results for the privacy guarantee and provide simulations for the empirical failure rate of the theoretical results under typical computational limitations. We give simulations for the accuracy of linear regression coefficients generated from the synthetic data compared with the accuracy of non-DP synthetic data and other DP methods. Additionally, our theoretical results extend a prior result for the sensitivity of the Gini Index to include continuous predictors.

关键词： Differential privacy Synthetic data Classification trees

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Symmetric vs Asymmetric Protection Levels in SDC Methods for Tabular Data

Symmetric vs Asymmetric Protection Levels in SDC Methods for...

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international conference on privacy in statistical databases (psd)

作者： Baena, Daniel Castro, Jordi Gonzalez, Jose A. Univ Politecn Cataluna Dept Stat & Operat Res Jordi Girona 1-3 Barcelona 08034 Catalonia Spain

ISBN: (纸本)9783319997704;9783319997711

Protection levels on sensitive cells-which are key parameters of any statistical disclosure control method for tabular data-are related to the difficulty of any attacker to recompute a good estimation of the true cell values. Those protection levels are two numbers (one for the lower protection, the other for the upper protection) imposing a safety interval around the cell value, that is, no attacker should be able to recompute an estimate within such safety interval. In the symmetric case the lower and upper protection levels are equal;otherwise they are referred as asymmetric protection levels. In this work we empirically study the effect of symmetry in protection levels for three protection methods: cell suppression problem (CSP), controlled tabular adjustment (CTA), and interval protection (IP). Since CSP and CTA are mixed integer linear optimization problems, it is seen that the symmetry (or not) of protection levels affect to the CPU time needed to compute a solution. For IP, a linear optimization problem, it is observed that the symmetry heavily affects to the quality of the solution provided rather than to the solution time.

关键词： statistical disclosure control Tabular data Cell suppression Controlled tabular adjustment Interval protection Mixed integer linear optimization Linear optimization

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Quantifying the Protection Level of a Noise Candidate for Noise Multiplication Masking Scheme

Quantifying the Protection Level of a Noise Candidate for No...

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international conference on privacy in statistical databases (psd)

作者： Ma, Yue Lin, Yan-Xia Krivitsky, Pavel N. Wakefield, Bradley Univ Wollongong Sch Math & Appl Stat Natl Inst Appl Stat Res Australia Wollongong NSW 2500 Australia

ISBN: (纸本)9783319997704;9783319997711

When multiplicative noises are used to perturb a set of original data, the data provider needs to ensure that the original values are not likely to be learned by data intruders from the noise-multiplied data. Different attacking strategies for unveiling the original values have been recognised in the literature, and the data provider needs to ensure that the noise-multiplied data is protected against these attacking strategies by selecting an appropriate noise generating variable. However, there are many potential attacking strategies, which makes the quantification of the protection level of a noise candidate difficult. In this paper, we argue that, to quantify the protection level a noise candidate offers to the original data against an attacking strategy, the data provider might look at the average value disclosure risk it produces. Correspondingly, we propose an optimal estimator which maximizes the average value disclosure risk. As a result, the data provider could use the maximized average value disclosure risk as a single measure for quantifying the protection level a noise candidate offers to the original data. The measure could help the data provider with the process of noise generating variable selection in practice.

关键词： Multiplicative noise masking Value disclosure risk Attacking strategy statistical disclosure control

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Grouping of Variables to Facilitate SDL Methods in Multivariate Data Sets

Grouping of Variables to Facilitate SDL Methods in Multivari...

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international conference on privacy in statistical databases (psd)

作者： Oganian, Anna Iacob, Ionut Lesaja, Goran Natl Ctr Hlth Stat 3311 Toledo Rd Hyattsville MD 20782 USA Georgia Southern Univ Dept Math Sci POB 8093 Statesboro GA 30460 USA

ISBN: (纸本)9783319997704;9783319997711

Data sets that are subject to statistical Disclosure Limitation (SDL) often have many variables of different types that need to be altered for disclosure limitation. To produce a good quality public data set, the data protector needs to account for the relationships between the variables. Hence, ideally SDL methods should not be univariate, that is, treating each variable independently of others, but multivariate, handling many variables at the same time. However, if a data set has many variables, as most government survey data do, the task of developing and implementing a multivariate approach for SDL becomes difficult. In this paper we propose a pre-masking data processing procedure which consists of clustering the variables of high dimensional data sets, so that different groups of variables can be masked independently, thus reducing the complexity of SDL. We consider different hierarchical clustering methods, including our version of hierarchical clustering algorithm, that we call K-Link, and outline how the data protector can define an appropriate number of clusters for these methods. We implemented and applied these methods to two genuine multivariate data sets. The results of the experiments show that K-Link has a potential to solve this problem efficiently. The success of the method, however, depends on the correlation structure of the data. For the data sets where most of the variables are correlated, clustering of variables and subsequent independent application of SDL methods to different clusters may lead to attenuated correlation in the masked data, even for efficient clustering methods. Thereby, the proposed approach is a trade-off between the computational complexity of multivariate SDL methods and data utility loss due to independent treatment of different clusters by SDL methods.

关键词： statistical Disclosure Limitation (SDL) Hierarchical clustering Dimensionality reduction

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privacy in statistical databases:UNESCO chair in data privacy international conference, psd 2014 ibiza, spain, september 17-19, 2014 proceedings

Privacy in statistical databases:UNESCO chair in data privac...

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international conference on privacy in statistical databases, psd 2014

作者： Domingo-Ferrer, Josep Universitat Rovira i Virgili Department of Computer Engineering and Mathematics UNESCO Chair in Data Privacy Av. Països Catalans 26 Tarragona Spain

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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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Computational issues in the design of transition probabilities and disclosure risk estimation for additive noise

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9867 LNCS卷 237-251页

作者： Giessing, Sarah Federal Statistical Office of Germany Wiesbaden65180 Germany

ISBN: (纸本)9783319453804

The Australian Bureau of Statistics has developed an additive noise method for automatically and consistently confidentialising tables of counts ‘on the fly’. statistical properties of the perturbation are defined by a matrix of transition probabilities. The present paper looks at mathematical and computational aspects of an approach mentioned in the literature for how to design those probabilities. In the second part, the paper proposes computation of feasibility intervals as a technique to compare the effects of different variants for post-tabular perturbative protection methods on disclosure risk and provides experimental results. © Springer international Publishing Switzerland 2016.

关键词： Additive noise

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