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共 130 条记录，以下是91-100 订阅

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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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Enhancing the efficiency in privacy preserving learning of decision trees in partitioned databases

Enhancing the efficiency in privacy preserving learning of d...

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international conference on privacy in statistical databases, psd 2012

作者： Lory, Peter University of Regensburg D-93040 Regensburg Germany

ISBN: (纸本)9783642336263

This paper considers a scenario where two parties having private databases wish to cooperate by computing a data mining algorithm on the union of their databases without revealing any unnecessary information. In particular, they want to apply the decision tree learning algorithm ID3 in a privacy preserving manner. Lindell and Pinkas (2002) have presented a protocol for this purpose, which enjoys a formal proof of privacy and is considerably more efficient than generic solutions. The crucial point of their protocol is the approximation of the logarithm function by a truncated Taylor series. The present paper improves this approximation by using a suitable Chebyshev expansion. This approach results in a considerably more efficient new version of the protocol. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Decision trees

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Security of random output perturbation for statistical databases

Security of random output perturbation for statistical datab...

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international conference on privacy in statistical databases, psd 2012

作者： Zanger, Daniel Z. SRI International Arlington VA United States

ISBN: (纸本)9783642336263

We prove that, with respect to a database query response privacy mechanism employing output perturbation with i.i.d. random noise addition, an adversary can, allowed a sufficiently large number of queries, exactly determine all records in an n-record database up to overwhelming probability of success, and establish corresponding quantitative confidence bounds for the attack success probability. These confidence bounds do not depend on the cardinality |D| of the data domain D∈∈R, where the database is a member of the set D n, and they even admit some unbounded data domains D of (countably) infinite cardinality. Within the context of differential privacy, we show that our results also imply a lower bound on the variance of independent, Laplace-distributed noise that can be added to user queries if database privacy is to be preserved. Our results do not require the additive noise to be bounded by as assumed in Dinur & Nissim (2003) and Dwork & Yekhanin (2008), which, on the other hand, do admit correlated noise. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Additive noise

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Valid statistical inference on automatically matched files

Valid statistical inference on automatically matched files

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international conference on privacy in statistical databases, psd 2012

作者： Hall, Rob Fienberg, Stephen Department of Statistics and Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9783642336263

We develop a statistical process for determining a confidence set for an unknown bipartite matching. It requires only modest assumptions on the nature of the distribution of the data. The confidence set involves a set of linear constraints on the bipartite matching, which permits efficient analysis of the matched data, e.g., using linear regression, while maintaining the proper degree of uncertainty about the linkage itself. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Uncertainty analysis

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Empirical evaluation of statistical inference from differentially-private contingency tables

Empirical evaluation of statistical inference from different...

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international conference on privacy in statistical databases, psd 2012

作者： Charest, Anne-Sophie Université Laval Canada

ISBN: (纸本)9783642336263

In this paper, we evaluate empirically the quality of statistical inference from differentially-private synthetic contingency tables. We compare three methods: histogram perturbation, the Dirichlet-Multinomial synthesizer and the Hardt-Ligett-McSherry algorithm. We consider a goodness-of-fit test for models suitable to the real data, and a model selection procedure. We find that the theoretical guarantees associated with these differentially-private datasets do not always translate well into guarantees about the statistical inference on the synthetic datasets. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Quality control

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