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检索条件"任意字段=International Conference on Privacy in Statistical Databases, PSD 2022"

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A Partitioned Recoding Scheme for privacy Preserving Data Publishing

A Partitioned Recoding Scheme for Privacy Preserving Data Pu...

引用

international conference on privacy in statistical databases (psd)

作者： Clifton, Chris Hanson, Eric J. Merrill, Keith Merrill, Shawn Zahraa, Amjad Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ CERIAS W Lafayette IN 47907 USA Brandeis Univ Dept Math Waltham MA 02453 USA

ISBN: (纸本)9783030575205;9783030575212

There is growing interest in Differential privacy as a disclosure limitation mechanism for statistical data. The increased attention has brought to light a number of subtleties in the definition and mechanisms. We explore an interesting dichotomy in parallel composition, where a subtle difference in the definition of a "neighboring database" leads to significantly different results. We show that by "pre-partitioning" the data randomly into disjoint subsets, then applying well-known anonymization schemes to those pieces, we can eliminate this dichotomy. This provides potential operational benefits, with some interesting implications that give further insight into existing privacy schemes. We explore the theoretical limits of the privacy impacts of pre-partitioning, in the process illuminating some subtle distinctions in privacy definitions. We also discuss the resulting utility, including empirical evaluation of the impact on released privatized statistics.

关键词： privacy-preserving techniques

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Advantages of Imputation vs. Data Swapping for statistical Disclosure Control

Advantages of Imputation vs. Data Swapping for Statistical D...

引用

international conference on privacy in statistical databases (psd)

作者： Kinney, Satkartar K. Looby, Charlotte B. Yu, Feng RTI Int Res Triangle Pk NC 27709 USA

ISBN: (纸本)9783030575205;9783030575212

Data swapping is an approach long-used by public agencies to protect respondent confidentiality in which values of some variables are swapped with similar records for a small portion of respondents. Synthetic data is a newer method in which many if not all values are replaced with multiple imputations. Synthetic data can be difficult to implement for complex data;however, when the portion of data replaced is similar to data swapping, it becomes simple to implement using publicly available software. This paper describes how this simplification of synthetic data can be used to provide a better balance of data quality and disclosure protection compared to data swapping. This is illustrated via an empirical comparison using data from the Survey of Earned Doctorates.

关键词： Synthetic data Applications Methodology

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Harmonizing privacy Regarding Data Retention and Purging 22

Harmonizing Privacy Regarding Data Retention and Purging

引用

34th international conference on Scientific and statistical Database Management (SSDBM)

作者： Scope, Nick Rasin, Alexander Lenard, Ben Heart, Karen Wagner, James Depaul Univ Chicago IL 60604 USA Univ New Orleans New Orleans LA 70148 USA

ISBN: (纸本)9781450396677

Data privacy requirements are a complex and quickly evolving part of the data management domain. Especially in Healthcare (e.g., United States Health Insurance Portability and Accountability Act and Veterans Affairs requirements), there has been a strong emphasis on data privacy and protection. Data storage is governed by multiple sources of policy requirements, including internal policies and legal requirements imposed by external governing organizations. Within a database, a single value can be subject to multiple requirements on how long it must be preserved and when it must be irrecoverably destroyed. This often results in a complex set of overlapping and potentially conflicting policies. Existing storage systems are lacking sufficient support functionality for these critical and evolving rules, making compliance an underdeveloped aspect of data management. As a result, many organizations must implement manual ad-hoc solutions to ensure compliance. As long as organizations depend on manual approaches, there is an increased risk of non-compliance and threat to customer data privacy. In this paper, we detail and implement an automated comprehensive data management compliance framework facilitating retention and purging compliance within a database management system. This framework can be integrated into existing databases without requiring changes to existing business processes. Our proposed implementation uses SQL to set policies and automate compliance. We validate this framework on a Postgres database, and measure the factors that contribute to our reasonable performance overhead (13% in a simulated real-world workload).

关键词： privacy compliance data governance retention purging

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Utility-Enhancing Flexible Mechanisms for Differential privacy

Utility-Enhancing Flexible Mechanisms for Differential Priva...

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international conference on privacy in statistical databases (psd)

作者： Mugunthan, Vaikkunth Xiao, Wanyi Kagal, Lalana MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9783030575205;9783030575212

Differential privacy is a mathematical technique that provides strong theoretical privacy guarantees by ensuring the statistical indistinguishability of individuals in a dataset. It has become the de facto framework for providing privacy-preserving data analysis over statistical datasets. Differential privacy has garnered significant attention from researchers and privacy experts due to its strong privacy guarantees. However, the accuracy loss caused by the noise added has been an issue. First, we propose a new noise adding mechanism that preserves (epsilon, delta)-differential privacy. The distribution pertaining to this mechanism can be observed as a generalized truncated Laplacian distribution. We show that the proposed mechanism adds optimal noise in a global context, conditional upon technical lemmas. We also show that the generalized truncated Laplacian mechanism performs better than the optimal Gaussian mechanism. In addition, we also propose an (epsilon)-differentially private mechanism to improve the utility of differential privacy by fusing multiple Laplace distributions. We also derive the closed-form expressions for absolute expectation and variance of noise for the proposed mechanisms. Finally, we empirically evaluate the performance of the proposed mechanisms and show an increase in all utility measures considered, while preserving privacy.

关键词： Differential privacy Generalized truncated Laplacian distribution Merging of Laplacian distributions

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Differential privacy and Its Applicability for Official Statistics in Japan - A Comparative Study Using Small Area Data from the Japanese Population Census

Differential Privacy and Its Applicability for Official Stat...

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international conference on privacy in statistical databases (psd)

作者： Ito, Shinsuke Miura, Takayuki Akatsuka, Hiroto Terada, Masayuki Chuo Univ Hachioji Tokyo Japan NTT Secure Platform Labs Musashino Tokyo Japan NTT DOCOMO Inc Yokosuka Kanagawa Japan

ISBN: (纸本)9783030575205;9783030575212

As part of its preparations for the 2020 U.S. Population Census, the U.S. Census Bureau uses themethodology of differential privacy to create privacy-preserved official microdata. It is expected that the use of differential privacy for official statistical data will become a topic in Japan in the future. In this paper, we survey the current discussion on the use of differential privacy for creating official statistical data. We describe the differential privacy method used by the U.S. Census Bureau, develop a method to apply differential privacy to Japanese population grid data, and conduct a comparison between different differential privacy methods by applying them to Japanese small area data. Results demonstrate that for population grid data, both the non-negative Wavelet method (and its public-n variant) and the US census top-down method preserve the non-negativity and sparsity of the original data, but each results in a different degree of error. This provides an important criterion for choosing the most appropriate method among the three options.

关键词： Differential privacy Population grid data Laplace mechanism Non-negative wavelet method Top-down census method 2D-Privelet

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Secure Matrix Computation: A Viable Alternative to Record Linkage?

Secure Matrix Computation: A Viable Alternative to Record Li...

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international conference on privacy in statistical databases (psd)

作者： Drechsler, Jorg Klein, Benjamin Inst Employment Res Regensburger Str 104 D-90478 Nurnberg Germany Ludwig Maximilians Univ Munchen Ludwigstr 33 D-80539 Munich Germany

ISBN: (纸本)9783030575205;9783030575212

Linking data from different sources can enrich the research opportunities in the Social Sciences. However, datasets can typically only be linked if the respondents consent to the linkage. Strategies from the secure multi-party computation literature, which do not require linkage on the record level, might be a viable alternative in this context to avoid biases due to selective non-consent. In this paper, we evaluate whether such a strategy could actually be successfully applied in practice by replicating a study based on linked data available at the Institute for Employment Research. We find that almost identical results could be obtained without the requirement to link the data. However, we also identify several problems suggesting that the proposed strategy might not be feasible in many practical contexts without further modification.

关键词： Confidentiality Record linkage Secure multi-party computation privacy Oaxaca-Blinder decomposition

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Integrating Differential privacy in the statistical Disclosure Control Tool-Kit for Synthetic Data Production

Integrating Differential Privacy in the Statistical Disclosu...

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international conference on privacy in statistical databases (psd)

作者： Shlomo, Natalie Univ Manchester Sch Social Sci Social Stat Dept Humanities Bridgeford St G17A Manchester M13 9PL Lancs England

ISBN: (纸本)9783030575205;9783030575212

A standard approach in the statistical disclosure control tool-kit for producing synthetic data is the procedure based on multivariate sequential chained-equation regression models where each successive regression includes variables from the preceding regressions. The models depend on conditional Bayesian posterior distributions and can handle continuous, binary and categorical variables. Synthetic data are generated by drawing random values from the corresponding predictive distributions. Multiple copies of the synthetic data are generated and inference carried out on each of the data sets with results combined for point and variance estimates under well-established combination rules. In this paper, we investigate whether algorithms and mechanisms found in the differential privacy literature can be added to the synthetic data production process to raise the privacy standards used at National statistical Institutes. In particular, we focus on a differentially private functional mechanism of adding random noise to the estimating equations of the regression models. We also incorporate regularization in the OLS linear models (ridge regression) to compensate for noisy estimating equations and bound the global sensitivity. We evaluate the standard and modified multivariate sequential chained-equation regression approach for producing synthetic data in a small-scale simulation study.

关键词： Sequential chained-equation regression models Functional mechanism Score functions Ridge regression

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Disclosure Avoidance in the Census Bureau's 2010 Demonstration Data Product

Disclosure Avoidance in the Census Bureau's 2010 Demonstrati...

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international conference on privacy in statistical databases (psd)

作者： Van Riper, David Kugler, Tracy Ruggles, Steven Univ Minnesota Minnesota Populat Ctr Minneapolis MN 55455 USA

ISBN: (纸本)9783030575205;9783030575212

Producing accurate, usable data while protecting respondent privacy are dual mandates of the US Census Bureau. In 2019, the Census Bureau announced it would use a new disclosure avoidance technique, based on differential privacy, for the 2020 Decennial Census of Population and Housing [19]. Instead of suppressing data or swapping sensitive records, differentially private methods inject noise into counts to protect privacy. Unfortunately, noise injection may also make the data less useful and accurate. This paper describes the differentially private Disclosure Avoidance System (DAS) used to prepare the 2010 Demonstration Data Product (DDP). It describes the policy decisions that underlie the DAS and how the DAS uses those policy decisions to produce differentially private data. Finally, it discusses usability and accuracy issues in the DDP, with a focus on occupied housing unit counts. Occupied housing unit counts in the DDP differed greatly from 2010 Summary File 1 differed greatly, and the paper explains possible sources of the differences.

关键词： Differential privacy 2020 US Decennial Census Accuracy

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Pα,β-privacy: A Composable Formulation of privacy Guarantees for Data Publishing Based on Permutation

Pα,β-Privacy: A Composable Formulation of Privacy Guarante...

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international conference on privacy in statistical databases (psd)

作者： Ruiz, Nicolas Univ Rovira & Virgili Dept Engn Informat & Matemat Av Paisos Catalans 26 Tarragona 43007 Catalonia Spain

ISBN: (纸本)9783030575205;9783030575212

Methods for privacy-Preserving Data Publishing (PPDP) have been recently shown to be equivalent to essentially performing some permutations of the original data. This insight, called the permutation paradigm, establishes a common ground upon which any method can be evaluated ex-post, but can also be viewed as a general ex-ante method in itself, where data are anonymized with the injection of suitable permutation matrices. It remains to develop around this paradigm a formal privacy model based on permutation. Such model should be sufficiently intuitive to allow non-experts to understand what it really entails for privacy to permute, in the same way that the privacy principles lying behind k-anonymity and differential privacy can be grasp by most. Moreover, similarly to differential privacy this model should ideally exhibit simple composition properties, which are highly handy in practice. Based on these requirements, this paper proposes a new privacy model for PPDP called P-alpha,P-beta-privacy. Using for benchmark a one-time pad, an absolutely secure encryption method, this model conveys a reasonably intuitive meaning of the privacy guarantees brought by permutation, can be used ex-ante or ex-post, and exhibits simple composition properties. We illustrate the application of this new model using an empirical example.

关键词： privacy-Preserving Data Publishing Permutation paradigm privacy model One-time pad

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A Bayesian Nonparametric Approach to Differentially Private Data

A Bayesian Nonparametric Approach to Differentially Private ...

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international conference on privacy in statistical databases (psd)

作者： Ayed, Fadhel Battiston, Marco Di Benedetto, Giuseppe Univ Oxford Oxford OX1 4BH England Univ Lancaster Lancaster LA1 4YW England

ISBN: (纸本)9783030575205;9783030575212

The protection of private and sensitive data is an important problem of increasing interest due to the vast amount of personal data collected. Differential privacy is arguably the most dominant approach to address privacy protection, and is currently implemented in both industry and government. In a decentralized paradigm, the sensitive information belonging to each individual will be locally transformed by a known privacy-maintaining mechanism Q. The objective of differential privacy is to allow an analyst to recover the distribution of the raw data, or some functionals of it, while only having access to the transformed data. In this work, we propose a Bayesian nonparametric methodology to perform inference on the distribution of the sensitive data, reformulating the differentially private estimation problem as a latent variable Dirichlet Process mixture model. This methodology has the advantage that it can be applied to any mechanism Q and works as a "black box" procedure, being able to estimate the distribution and functionals thereof using the same MCMC draws and with very little tuning. Also, being a fully non-parametric procedure, it requires very little assumptions on the distribution of the raw data. For the most popular mechanisms Q, like Laplace and Gaussian, we describe efficient specialized MCMC algorithms and provide theoretical guarantees. Experiments on both synthetic and real dataset show a good performance of the proposed method.

关键词： Differential privacy Bayesian Nonparametrics Exponential mechanism Laplace noise Dirichlet Process mixture model Latent variables

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