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Improving the Utility of Differential privacy via Univariate Microaggregation

Improving the Utility of Differential Privacy via Univariate...

引用

UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： Sanchez, David Domingo-Ferrer, Josep Martinez, Sergio Univ Rovira & Virgili Dept Comp Engn & Math UNESCO Chair Data Privacy E-43007 Tarragona Spain

ISBN: (纸本)9783319112572;9783319112565

Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted type of queries. On the contrary, k-anonymity-like anonymization offers general purpose data releases that make no assumption on the uses of the protected data. This paper proposes a mechanism to offer general purpose differentially private data releases with a specific focus on the preservation of the utility of the protected data. Our proposal relies on univariate microaggregation to reduce the amount of noise needed to satisfy differential privacy. The theoretical benefits of the proposal are illustrated and in a practical setting.

关键词： privacy-preserving data publishing Differential privacy Microaggregation Data utility

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Using Partially Synthetic Data to Replace Suppression in the Business Dynamics Statistics: Early Results

Using Partially Synthetic Data to Replace Suppression in the...

引用

UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： Miranda, Javier Vilhuber, Lars US Bur Census Washington DC 20233 USA Cornell Univ Ithaca NY 14850 USA

ISBN: (纸本)9783319112572;9783319112565

The Business Dynamics Statistics is a product of the U.S. Census Bureau that provides measures of business openings and closings, and job creation and destruction, by a variety of cross-classifications (firm and establishment age and size, industrial sector, and geography). Sensitive data are currently protected through suppression. However, as additional tabulations are being developed, at ever more detailed geographic levels, the number of suppressions increases dramatically. This paper explores the option of providing public-use data that are analytically valid and without suppressions, by leveraging synthetic data to replace observations in sensitive cells.

关键词： synthetic data statistical disclosure limitation time-series local labor markets gross job flows confidentiality protection

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Comparison of Two Remote Access Systems Recently Developed and Implemented in Australia

Comparison of Two Remote Access Systems Recently Developed a...

引用

UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： O'Keefe, Christine M. Gould, Phillip Churches, Tim CSIRO Computat Informat Canberra ACT 2601 Australia Australian Bur Stat Belconnen ACT 2616 Australia Sax Inst Haymarket NSW 1240 Australia

ISBN: (纸本)9783319112572;9783319112565

National statistical Agencies and other data custodians are vital sources of data for research and policy analysis. However, external researchers must be provided with access to data in such a way that privacy and confidentiality are protected. We discuss two recently-implemented research data access systems. The first was developed by the Australian Bureau of Statistics for use with certain of its data collections. The second was developed by the Sax Institute, a non-profit health research non-government organisation, for use by population health and health services researchers to analyse complex, linked administrative health and related data sets provided by a range of data custodians. Although these organisations both chose remote access systems, it is interesting that there are significant differences between the two systems. We discuss the drivers for and consequences of the different choices made.

关键词： privacy Confidentiality Remote Access Remote Analysis

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Applicability of Confidentiality Methods to Personal and Business Data

Applicability of Confidentiality Methods to Personal and Bus...

引用

UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： O'Keefe, Christine M. Shlomo, Natalie CSIRO Computat Informat Canberra ACT 2601 Australia Univ Manchester Sch Social Sci Manchester M13 9PL Lancs England

ISBN: (纸本)9783319112572;9783319112565

National statistical Agencies and other data custodians have a responsibility to protect the confidentiality of commercially sensitive business data as well as personally private social and survey data. However, traditional confidentiality methods have generally been developed for the context of social or survey data about individual persons. Several recent studies have highlighted that such traditional confidentiality measures may not be directly applicable to business data, due to the different characteristics of business data and personal data. In this paper we provide a discussion of these recent studies and their conclusions. We find that while the confidentiality objective is the same for business data and social and survey data, the disclosure scenarios and disclosure risks are different. There is evidence that business data and social and survey data may require different confidentiality protection methods to achieve an effective balance between disclosure risk and data utility.

关键词： Confidentiality privacy Disclosure Control Business Data

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Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS databases

Differentially-Private Logistic Regression for Detecting Mul...

引用

UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： Yu, Fei Rybar, Michal Uhler, Caroline Fienberg, Stephen E. Carnegie Mellon Univ Pittsburgh PA 15213 USA IST Austria A-3400 Klosterneuburg Austria

ISBN: (纸本)9783319112572;9783319112565

Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way. Here, we develop an end-to-end differentially private method for solving regression problems with convex penalty functions and selecting the penalty parameters by cross-validation. In particular, we focus on penalized logistic regression with elastic-net regularization, a method widely used to in GWAS analyses to identify disease-causing genes. We show how a differentially private procedure for penalized logistic regression with elastic-net regularization can be applied to the analysis of GWAS data and evaluate our method's performance.

关键词： Differential privacy genome-wide association studies (GWAS) logistic regression elastic-net ridge regression lasso cross-validation single nucleotide polymorphism (SNP)

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Controlled Shuffling, statistical Confidentiality and Microdata Utility: A Successful Experiment with a 10% Household Sample of the 2011 Population Census of Ireland for the IPUMS-international Database

Controlled Shuffling, Statistical Confidentiality and Microd...

引用

UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： McCaa, Robert Muralidhar, Krishnamurty Sarathy, Rathindra Comerford, Michael Esteve-Palos, Albert Minnesota Populat Ctr Minneapolis MN 55455 USA

ISBN: (纸本)9783319112572;9783319112565

IPUMS-international disseminates more than two hundred-fifty integrated, confidentialized census microdata samples to thousands of researchers world-wide at no cost. The number of samples is increasing at the rate of several dozen per year, as quickly as the task of integrating metadata and microdata is completed. Protecting the statistical confidentiality and privacy of individuals represented in the microdata is a sine qua non of the IPUMS project. For the 2010 round of censuses, even greater protections are required, while researchers are demanding ever higher precision and utility. This paper describes a tripartite collaborative experiment using a ten percent household sample of the 2011 census of Ireland to estimate risk, mask the microdata using controlled shuffling, and assess analytical utility by comparing the masked data against the unprotected source microdata. Controlled shuffling exploits hierarchically ordered coding schemes to protect privacy and enhance utility. With controlled shuffling, the lesson seems to be the more detail means less risk and greater utility. Overall, despite substantial perturbation of the masked dataset (30% of adults on one or more characteristic), we find that data utility is very high and information loss is slight, even for fairly complex analytical problems.

关键词： controlled shuffling population census microdata sample data privacy data utility statistical disclosure controls IPUMS-international Ireland

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Comparison of Different Sensitivity Rules for Tabular Data and Presenting a New Rule - The Interval Rule

Comparison of Different Sensitivity Rules for Tabular Data a...

引用

UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： Bring, Johan Wang, Qun Stat Sweden Stockholm Sweden

ISBN: (纸本)9783319112572;9783319112565

statistical disclosure control (SDC) is a set of methods that are used to reduce the risk of disclosing information on individuals, businesses or other organisations. The focus of this paper is on sensitivity rules, which deal with how to define whether a cell in tabular data has the risk of disclosing information or not. The current popular sensitivity rules include the dominance rule and the P% rule. There is a weakness with these rules and a new rule - the interval rule is presented. The main argument for this new rule is that the rule should only be based on the information that the intruder knows, not on the information that the statistical institution knows. Based on simulated data, the P% rule tends to classify a dataset to be "sensitive" when it contains only one observation with a very large value. In this respect, and the dominance rule and the P% rule share a lot in common. Meanwhile the interval rule tends to classify a dataset to be "sensitive" when it contains two observations with large values.

关键词： statistical disclosure control tabular data sensitivity rules P% rule dominance rule interval rule

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Towards Secure and Practical Location privacy through Private Equality Testing

Towards Secure and Practical Location Privacy through Privat...

引用

UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： Magkos, Emmanouil Kotzanikolaou, Panayiotis Magioladitis, Marios Sioutas, Spyros Verykios, Vassilios S. Ionian Univ Dept Informat Kerkyra 49100 Greece Univ Piraeus Dept Informat Piraeus 18534 Greece Hellen Open Univ Sch Sci & Technol GR-26335 Patras Greece

ISBN: (纸本)9783319112572;9783319112565

In this paper, we propose a practical, privacy-preserving equality testing primitive which allows two users to learn if they share the same encrypted input data. Our protocol assumes no trust on a third party and/or other peers, and it is specifically suited for low-min entropy data (i.e., data that can be exhaustively searched by an attacker), such as encrypted users locations. We demonstrate that our primitive is secure and efficient: Two public-key exponentiations are required, per each user, for each equality testing. We give implementation results, showing that our primitive is practical in a multiple users scenario. Finally, we describe how we could use our primitive as a building block for a proximity testing buddy-finder service for social networks.

关键词： Equality testing Location privacy Buddy-finder social network Location-based services Geo-social applications

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A CTA Model Based on the Huber Function

A CTA Model Based on the Huber Function

引用

UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： Castro, Jordi Univ Politecn Cataluna Dept Stat & Operat Res ES-08034 Barcelona Spain

ISBN: (纸本)9783319112572;9783319112565

Minimum distance controlled tabular adjustment (CTA) is an emerging perturbative method of statistical disclosure control for tabular data. The goal of CTA is to find the closest safe table to some original tabular data with sensitive information. Closeness is usually measured by l(1) or l(2) distances. Distance l(1) provides solutions with a smaller l(0) norm than l(2) (i.e., with a lesser number of changes with respect to the original table). However the optimization problem formulated with l(2) requires half the number of variables than that for l(1), and it is more efficiently solved. In this work a pseudo-Huber function (which is a continuous nonlinear approximation of the Huber function) is considered to measure the distance between the original and protected tables. This pseudo-Huber function approximates l(1) but can be formulated with the same number of variables than l(2). It results in a nonlinear convex optimization problem which, theoretically, can be solved in polynomial time. Some preliminary results using the Huber-CTA model are reported.

关键词： statistical disclosure control controlled tabular adjustment nonlinear optimization convex optimization interior-point methods Huber loss function pseudo-Huber loss function

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k^m-Anonymity for Continuous Data Using Dynamic Hierarchies

<i>k<SUP>m</SUP></i>-Anonymity for Continuous Data Using Dyn...

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UNESCO Chair in Data privacy international conference on privacy in statistical databases (psd)

作者： Gkountouna, Olga Angeli, Sotiris Zigomitros, Athanasios Terrovitis, Manolis Vassiliou, Yannis Natl Tech Univ Athens GR-10682 Athens Greece Inst Management Informat Syst IMIS Athens Greece Univ Piraeus Piraeus Greece

ISBN: (纸本)9783319112572;9783319112565

Many organizations, enterprises or public services collect and manage personal data of individuals. These data contain knowledge that is of substantial value for scientists and market experts, but carelessly disseminating them can lead to significant privacy breaches, as they might reveal financial, medical or other personal information. Several anonymization methods have been proposed to allow the privacy preserving sharing of datasets with personal information. Anonymization techniques provide a trade-off between the strength of the privacy guarantee and the quality of the anonymized dataset. In this work we focus on the anonymization of sets of values from continuous domains, e.g., numerical data, and we provide a method for protecting the anonymized data from attacks against identity disclosure. The main novelty of our approach is that instead of using a fixed, given generalization hierarchy, we let the anonymization algorithm decide how different values will be generalized. The benefit of our approach is twofold: a) we are able to generalize datasets without requiring an expert to define the hierarchy and b) we limit the information loss, since the proposed algorithm is able to limit the scope of the generalization. We provide a series of experiments that demonstrate the gains in terms of information quality of our algorithm compared to the state-of-the-art.

关键词： privacy-Preserving Data Publishing privacy k(m)-anonymity Continuous data

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