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A proposed solution to the archiving and curation of confidential scientific inputs

A proposed solution to the archiving and curation of confide...

引用

international conference on privacy in statistical databases, psd 2012

作者： Abowd, John M. Vilhuber, Lars Block, William Department of Economics and Labor Dynamics Institute Cornell University Ithaca NY United States Cornell Institute for Social and Economic Research Cornell University Ithaca NY United States

ISBN: (纸本)9783642336263

We develop the core of a method for solving the data archive and curation problem that confronts the custodians of restricted-access research data and the scientific users of such data. Our solution recognizes the dual protections afforded by physical security and access limitation protocols. It is based on extensible tools and can be easily incorporated into existing instructional materials. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： privacy-preserving techniques

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Clustering-based categorical data protection

Clustering-based categorical data protection

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international conference on privacy in statistical databases, psd 2012

作者： Marés, Jordi Torra, Vicenç Campus de la UAB 08193 Bellaterra Catalonia Spain

ISBN: (纸本)9783642336263

The need of improving the privacy on public datasets is becoming more and more important because the number of public available datasets is growing very fast. This forced the continuous research to find better protection methods that prevent the disclosure of the entities or individuals in a dataset while preserving the data utility. In this paper we present a new approach for categorical data protection based on applying clustering to the dataset and then protecting each cluster. We show that this new approach allow us to have protections with better trade-off between data utility and individuals information disclosure. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Data privacy

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When excessive perturbation goes wrong and why IPUMS-international relies instead on sampling, suppression, swapping, and other minimally harmful methods to protect privacy of census microdata

When excessive perturbation goes wrong and why IPUMS-interna...

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international conference on privacy in statistical databases, psd 2012

作者： Cleveland, Lara McCaa, Robert Ruggles, Steven Sobek, Matthew Minnesota Population Center 50 Willey Hall Minneapolis MN 55455 United States

ISBN: (纸本)9783642336263

IPUMS-international disseminates population census microdata at no cost for 69 countries. Currently, a series of 212 samples totaling almost a half billion person records are available to researchers. Registration is required for researchers to gain access to the microdata. Statistics from Google Analytics show that IPUMS-international's lengthy, probing registration form is an effective deterrent for unqualified applicants. To protect data privacy, we rely principally on sampling, suppression of geographic detail, swapping of records across geographic boundaries, and other minimally harmful methods such as top and bottom coding. We do not use excessively perturbative methods. A recent case of perturbation gone wrongthe household samples of the 2000 census of the USA (PUMS), the 2003-2006 American Community Survey, and the 2004-2009 Current Population Survey, an empirical study of the impact of perturbation on the usability of UK census microdatathe Individual SARs of the 1991 census of the UK, and a mathematical demonstration in a timely compendium of statistical confidentiality practices confirm the wisdom of IPUMS microdata management protocols and statistical disclosure controls. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Data privacy

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Flexible rounding based on consistent post-tabular stochastic noise

Flexible rounding based on consistent post-tabular stochasti...

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international conference on privacy in statistical databases, psd 2012

作者： Giessing, Sarah Federal Statistical Office of Germany 65180 Wiesbaden Germany

ISBN: (纸本)9783642336263

Many statistical agencies nowadays operate or envision tools for ad hoc creation and visualization of aggregate tables. Such tools can indeed increase the efficiency of those parts of the data production process that involve creating tables customized to user queries, if disclosure control is an integrated component. Especially in the case of business data certain disadvantages of traditional methods like cell suppression become critical in such a context. Literature has discussed alternative ideas based on stochastic disclosure limitation methods like pre-tabular multiplicative noise [1], or post-tabular additive noise [2]. As an extension of the latter, post-tabular multiplicative noise has been introduced in [4].The present paper elaborates further on this approach. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Additive noise

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European data access comes true decentralised and remote access to confidential data in the ESS (ESSnet DARA)

European data access comes true decentralised and remote acc...

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international conference on privacy in statistical databases, psd 2012

作者： Brandt, Maurice Federal Statistical Office Germany Gustav-Stresemann-Ring 11 65189 Wiesbaden Germany

ISBN: (纸本)9783642336263

The question of microdata access is solved in most of the EU Member States and the access to national data is basically possible in one or another way. This infrastructure can be used now to satisfy the strong demand for cross country analysis for comparison and evaluation of the developments between EU Member States. There are several goals and ambitions to establish a cross boarder access to European Statistics. The project Decentralised and Remote Access to Confidential Data in the ESS is going to deliver a running remote access system from the access facility in the National statistical Institutes of the Member States to the European statistics at Eurostat within the next year. Before that, technical, methodological, organisational and legal aspects need to be solved. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Statistics

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On computational anonymity

On computational anonymity

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international conference on privacy in statistical databases, psd 2012

作者： Stokes, Klara Department of Computer Engineering and Mathematics Universitat Rovira i Virgili UNESCO in Data Privacy Av. Països Catalans 26 E-43007 Tarragona Catalonia Spain Universitat Oberta de Catalunya Roc Boronat 117 7a. planta E-08018 Barcelona Catalonia Spain

ISBN: (纸本)9783642336263

The concern of data privacy is to mask data so that they can be transferred to untrusted third parties without leaking confidential individual information. In this work we distinguish between theoretical anonymity and computational anonymity. We present a relaxation of k-anonymity, called (k,l)-anonymity, which makes sense when it can be assumed that the knowledge of an adversary is limited. (k,l)-Anonymity can also be regarded as a quantification of the anonymity in terms of the adversary's limitations. Combinatorics, or more precisely, hypergraphs, are used to represent the anonymity relations in a (k,l)-anonymous table. Finally, we present an algorithm for the (k,l)-anonymization of tables. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Data privacy

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Hybrid microdata via model-based clustering

Hybrid microdata via model-based clustering

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international conference on privacy in statistical databases, psd 2012

作者： Oganian, Anna Domingo-Ferrer, Josep Department of Mathematical Sciences Georgia Southern University P.O. Box 8093 Statesboro GA 30460-8093 United States Department of Computer Engineering and Maths Universitat Rovira i Virgili UNESCO in Data Privacy Av. Països Catalans 26 E-43007 Tarragona Catalonia Spain

ISBN: (纸本)9783642336263

In this paper we propose a new scheme for statistical disclosure limitation which can be classified as a hybrid method of protection, that is, a method that combines properties of perturbative and synthetic methods. This approach is based on model-based clustering with the subsequent synthesis of the records within each cluster. The novelty is that the clustering and synthesis methods have been carefully chosen to fit each other in view of reducing information loss. The model-based clustering tries to obtain clusters such that the within-cluster data distribution is approximately normal;then we can use a multivariate normal synthesizer for the local synthesis of data. In this way, some of the non-normal characteristics of the data are captured by the clustering, so that a simple synthesizer for normal data can be used within each cluster. Our method is shown to be effective when compared to other disclosure limitation strategies. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Maximum principle

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Better than nothing privacy with bloom filters: To what extent?

Better than nothing privacy with bloom filters: To what exte...

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international conference on privacy in statistical databases, psd 2012

作者： Bianchi, Giuseppe Bracciale, Lorenzo Loreti, Pierpaolo DIE Universitá di Roma Tor Vergata Rome Italy

ISBN: (纸本)9783642336263

Bloom filters are probabilistic data structures which permit to conveniently represent set membership. Their performance/memory efficiency makes them appealing in a huge variety of scenarios. Their probabilistic operation, along with the implicit data representation, yields some ambiguity on the actual data stored, which, in scenarios where cryptographic protection is unviable or unpractical, may be somewhat considered as a better than nothing privacy asset. Oddly enough, even if frequently mentioned, to the best of our knowledge the (soft) privacy properties of Bloom filters have never been explicitly quantified. This work aims to fill this gap. Starting from the adaptation of probabilistic anonymity metrics to the Bloom filter setting, we derive exact and (tightly) approximate formulae which permit to readily relate privacy properties with filter (and universe set) parameters. Using such relations, we quantitatively investigate the emerging privacy/utility trade-offs. We finally preliminary assess the advantages that a tailored insertion of a few extra (covert) bits achieves over the commonly employed strategy of increasing ambiguity via addition of random bits. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Data structures

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Differential privacy Through Knowledge Refinement

Differential Privacy Through Knowledge Refinement

引用

ASE/IEEE international conference on privacy, Security, Risk and Trust / ASE/IEEE international conference on Social Computing (SocialCom/PASSAT)

作者： Soria-Comas, Jordi Domingo-Ferrer, Josep Univ Rovira & Virgili UNESCO Dept Comp Engn & Maths Chair Data Privacy Av Paisos Catalans 26 E-43007 Tarragona Spain

ISBN: (纸本)9780769548487;9781467356381

We introduce a novel mechanism to attain differential privacy. Contrary to the common mechanism based on the addition of a noise whose magnitude is proportional to the sensitivity of the query function, our proposal is based on the refinement of the user's prior knowledge about the response. We show that our mechanism has several advantages over noise addition: it does not require complex computations, and thus it can be easily automated;it lets the user exploit her prior knowledge about the response to achieve better data quality;and it is independent of the sensitivity of the query function (although this can be a disadvantage if the sensitivity is small). We also show some compounding properties of our mechanism for the case of multiple queries.

关键词： Differential privacy knowledge refinement statistical databases

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N-Cycle swapping for the american community survey

N-Cycle swapping for the american community survey

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international conference on privacy in statistical databases, psd 2012

作者： Depersio, Michael Lemons, Marlow Ramanayake, Kaleli A. Tsay, Julie Zayatz, Laura Center for Disclosure Avoidance Research U.S.Census Bureau Washington DC 20233 United States Center for Statistical Research and Methodology U.S.Census Bureau Washington DC 20233 United States

ISBN: (纸本)9783642336263

Data from the American Community Survey (ACS) are collected under the authority of Title 13 of the United States Code which guarantees the confidentiality of all survey respondents. To maintain this confidentiality while still being able to release usable data, the U.S. Census Bureau applies several disclosure avoidance methods. For the ACS, data swapping techniques are used to protect records deemed at risk. Households are identified as at risk if they are unique on attributes selected and predetermined by the confidentiality and survey management staff. After being uniquely identified (or flagged), data swapping is used to exchange the geographic information of a flagged household with another flagged household. This study compared the effectiveness of the pair-swapping method currently used with a proposed n-Cycle swapping method. Specifically, the goal was to maintain the same level of disclosure protection while outputting data with less perturbation. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Surveys

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