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Utility-Enhancing Flexible Mechanisms for Differential privacy

Utility-Enhancing Flexible Mechanisms for Differential Priva...

引用

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...

引用

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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Detecting Bad Answers in Survey Data Through Unsupervised Machine Learning

Detecting Bad Answers in Survey Data Through Unsupervised Ma...

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international conference on privacy in statistical databases (psd)

作者： Jebreel, Najeeb Moharram Haffar, Rami Singh, Ashneet Khandpur Sanchez, David Domingo-Ferrer, Josep Blanco-Justicia, Alberto Univ Rovira & Virgili CYBERCAT Ctr Cybersecur Res Catalonia Dept Comp Engn & Math UNESCO Chair Data Privacy Av Paisos Catalans 26 Tarragona 43007 Catalonia Spain

ISBN: (纸本)9783030575205;9783030575212

Surveys are one of the most common ways of collecting data on individuals. Such data are of great value for economic and social research. However, the quality of the decisions and research results based on survey data depends on the ability to detect and filter out bad answers. The most common source of bad data are the respondents, who might provide imprecise or fabricated answers due to several reasons. In this paper we present a method to sanitize survey data that relies on combining the classification outcomes of three unsupervised machine learning algorithms (DBSCAN, PCA and IForest) aimed at detecting bad answers. Empirical results on real data show that our approach is able to improve the detection of both completely and partially bad answers with respect to the results provided by each algorithm independently.

关键词： Survey data Data quality Bad answers Fabrication Unsupervised machine learning

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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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A Synthetic Supplemental Public Use File of Low-Income Information Return Data: Methodology, Utility, and privacy Implications

A Synthetic Supplemental Public Use File of Low-Income Infor...

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international conference on privacy in statistical databases (psd)

作者： Bowen, Claire McKay Bryant, Victoria Burman, Leonard Khitatrakun, Surachai McClelland, Robert Stallworth, Philip Ueyama, Kyle Williams, Aaron R. Urban Inst Washington DC 20024 USA Internal Revenue Serv Washington DC 20002 USA Syracuse Univ Syracuse NY 13244 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9783030575205;9783030575212

US government agencies possess data that could be invaluable for evaluating public policy, but often may not be released publicly due to disclosure concerns. For instance, the Statistics of Income division (SOI) of the Internal Revenue Service releases an annual public use file of individual income tax returns that is invaluable to tax analysts in government agencies, nonprofit research organizations, and the private sector. However, SOI has taken increasingly aggressive measures to protect the data in the face of growing disclosure risks, such as a data intruder matching the anonymized public data with other public information available in nontax databases. In this paper, we describe our approach to generating a fully synthetic representation of the income tax data by using sequential Classification and Regression Trees and kernel density smoothing. This synthetic data file represents previously unreleased information useful for tax policy modeling. We also tested and evaluated the tradeoffs between data utility and disclosure risks of different parameterizations using a variety of validation metrics. The resulting synthetic data set has high utility, particularly for summary statistics and microsimulation, and low disclosure risk.

关键词： Disclosure control Synthetic data Utility Classification and Regression Trees

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ε-Differential privacy for Microdata Releases Does Not Guarantee Confidentiality (Let Alone Utility)

ε-Differential Privacy for Microdata Releases Does Not Guar...

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international conference on privacy in statistical databases (psd)

作者： Muralidhar, Krishnamurty Domingo-Ferrer, Josep Martinez, Sergio Univ Oklahoma Dept Mkt & Supply Chain Management 307 West BrooksAdams Hall Room 10 Norman OK 73019 USA Univ Rovira & Virgili CYBERCAT Ctr Cybersecur Res Catalonia Unesco Chai Dept Comp Engn & Math Av Paisos Catalans 26 Tarragona 43007 Catalonia Spain

ISBN: (纸本)9783030575205;9783030575212

Differential privacy (DP) is a privacy model that was designed for interactive queries to databases. Its use has then been extended to other data release formats, including microdata. In this paper we show that setting a certain epsilon in DP does not determine the confidentiality offered by DP microdata, let alone their utility. Confidentiality refers to the difficulty of correctly matching original and anonymized data, and utility refers to anonymized data preserving the correlation structure of original data. Specifically, we present two methods for generating epsilon-differentially private microdata. One of them creates DP synthetic microdata from noise-added covariances. The other relies on adding noise to the cumulative distribution function. We present empirical work that compares the two new methods with DP microdata generation via prior microaggregation. The comparison is in terms of several confidentiality and utility metrics. Our experimental results indicate that different methods to enforce epsilon-DP lead to very different utility and confidentiality levels. Both confidentiality and utility seem rather dependent on the amount of permutation performed by the particular SDC method used to enforce DP. Thus suggests that DP is not a good privacy model for microdata releases.

关键词： Anonymized microdata Differential privacy Synthetic data Confidentiality Analytical utility

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