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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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Collaborative Analytics for Data Silos 32

Collaborative Analytics for Data Silos

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32nd IEEE international conference on Data Engineering (ICDE)

作者： Kim, Jinkyu Ha, Heonseok Chun, Byung-Gon Yoon, Sungroh Cha, Sang K. Seoul Natl Univ ECE Seoul 08826 South Korea Seoul Natl Univ CSE Seoul 08826 South Korea Seoul Natl Univ Big Data Inst Seoul 08826 South Korea Univ Calif Berkeley EECS Berkeley CA 94720 USA

ISBN: (纸本)9781509020201

As a great deal of data has been accumulated in various disciplines, the need for the integrative analysis of separate but relevant data sources is becoming more important. Combining data sources can provide global insight that is otherwise difficult to obtain from individual sources. Because of privacy, regulations, and other issues, many large-scale data repositories remain closed off from the outside, raising what has been termed the data silo issue. The huge volume of today's big data often leads to computational challenges, adding another layer of complexity to the solution. In this paper, we propose a novel method called collaborative analytics by ensemble learning (CABEL), which attempts to resolve the main hurdles regarding the silo issue: accuracy, privacy, and computational efficiency. CABEL represents the data stored in each silo as a compact aggregate of samples called the silo signature. The compact representation provides computational efficiency and privacy preservation but makes it challenging to produce accurate analytics. To resolve this challenge, we formulate the problem of attribute domain sampling and reconstruction, and propose a solution called the Chebyshev subset. To model collaborative efforts to analyze semantically linked but structurally disconnected databases, CABEL utilizes a new ensemble learning technique termed the weighted bagging of base classifiers. We demonstrate the effectiveness of CABEL by testing with a nationwide health-insurance data set containing approximately 4,182,000,000 records collected from the entire population of an Organisation for Economic Co-operation and Development (OECD) country in 2012. In our binary classification tests, CABEL achieved median recall, precision, and F measure values of 89%, 64%, and 76 %, respectively, although only 0.001-0.00001 % of the original data was used for model construction, while maintaining data privacy and computational efficiency.

关键词： statistical tests

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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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Spatial smoothing and statistical disclosure control

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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 107-117页

作者： De Jonge, Edwin De Wolf, Peter-Paul Statistics Netherlands The Hague Netherlands

ISBN: (纸本)9783319453804

Cartographic maps have many practical uses and can be an attractive alternative for disseminating detailed frequency tables. However, a detailed map may disclose private data of individual units of a population. We will describe some smoothing algorithms to display spatial distribution patterns. In certain situations, the disclosure risk of a spatial distribution pattern, can be formulated in terms of a frequency table disclosure problem. In this paper we will explore the effects of spatial smoothing related to statistical disclosure control. © Springer international Publishing Switzerland 2016.

关键词： Spatial distribution

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A rule-based approach to local anonymization for exclusivity handling in statistical databases

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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 81-93页

作者： Albrecht, Jens Fiedler, Marc Kiefer, Tim Technische Hochschule Nürnberg Fakultät Informatik Nürnberg Germany GfK SE Data and Technology Nürnberg Germany Technische Universität Dresden Database Systems Group Dresden Germany

ISBN: (纸本)9783319453804

statistical databases in general and data warehouses in particular are used to analyze large amounts of business data in predefined as well as ad-hoc reports. Operators of statistical databases must ensure that individual sensitive data, e.g., personal data, medical data, or business-critical data, are not revealed to unprivileged users while making use of these data in aggregates. Business rules must be defined and enforced to prevent disclosure. The unsupervised nature of ad-hoc reports, defined by the user and unknown to the database operator upfront, adds to the complexity of keeping data secure. Storing sensitive data in statistical databases demands automated methods to prevent direct or indirect disclosure of such sensitive data. This document describes a rule-based approach to local recoding of sensitive data. It introduces the notion of exclusivity to describe quasiidentifiers with local rules based on the multidimensional data model. It further defines options to treat exclusive entities that may disclose sensitive data. The local anonymization minimizes information loss and works well even with very large data sets. © Springer international Publishing Switzerland 2016.

关键词： Sensitive data

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On-average KL-privacy and its equivalence to generalization for max-entropy mechanisms

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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 121-134页

作者： Wang, Yu-Xiang Lei, Jing Fienberg, Stephen E. Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States Department of Statistics Carnegie Mellon University PittsburghPA15213 United States Heinz College Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9783319453804

We define On-Average KL-privacy and present its properties and connections to differential privacy, generalization and informationtheoretic quantities including max-information and mutual information. The new definition significantly weakens differential privacy, while preserving its minimal design features such as composition over small group and multiple queries as well as closeness to post-processing. Moreover, we show that On-Average KL-privacy is equivalent to generalization for a large class of commonly-used tools in statistics and machine learning that samples from Gibbs distributions-a class of distributions that arises naturally from the maximum entropy principle. In addition, a byproduct of our analysis yields a lower bound for generalization error in terms of mutual information which reveals an interesting interplay with known upper bounds that use the same quantity. © Springer international Publishing Switzerland 2016.

关键词： Information theory

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Enabling collaborative privacy in user-generated emergency reports

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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 255-271页

作者： Qureshi, Amna Rifà-Pous, Helena Megías, David Universitat Oberta de Catalunya Barcelona Spain

ISBN: (纸本)9783319453804

Witnesses are of the utmost importance in emergency systems since they can trigger timely location-based status alerts. However, their collaboration with the authorities can get impaired for the fear of the people of being involved with someone, some place, or even with the same cause of the emergency. Anonymous reporting solutions can encourage the witnesses, but they also pose a threat of system collapse if the authority receives many fake reports. In this paper, we propose an emergency reporting system that ensures the anonymity of honest witnesses but is able to disclose the identity and punish the malicious ones. The system is designed over an online social network that facilitates the indistinguishability of the witness among a group of users. We use a game-theoretic approach based on the co-privacy (co-utility) principles to encourage the users of the network to participate in the protocol. We also use discernible ring signatures to provide the property of conditional anonymity. In addition, the system is designed to provide rewards to a witness and his/her group members in a privacy-preserving manner. © Springer international Publishing Switzerland 2016.

关键词： Social networking (online)

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privacy in statistical databases - UNESCO Chair in Data privacy, international conference, psd 2012, Proceedings

Privacy in Statistical Databases - UNESCO Chair in Data Priv...

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international conference on privacy in statistical databases, psd 2012

ISBN: (纸本)9783642336263

The proceedings contain 27 papers. The topics discussed include: a computational evaluation of optimization solvers for CTA;flexible rounding based on consistent post-tabular stochastic noise;an investigation of model-based microdata masking for magnitude tabular data release;clustering-based categorical data protection;anonymization methods for taxonomic microdata;hybrid microdata via model-based clustering;logistic regression with variables subject to post randomization method;valid statistical inference on automatically matched files;generating useful test data for complex linked employer-employee datasets;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;achieving comparability of earnings;and designing multiple releases from the small and medium enterprises survey.

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