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Secure and highly-available aggregation queries in large-scale sensor networks via set sampling

DISTRIBUTED COMPUTING 2011年第5-6期23卷 373-394页

作者： Yu, Haifeng Natl Univ Singapore Sch Comp Singapore 117417 Singapore

Wireless sensor networks are often queried for aggregates such as predicate count, sum, and average. In untrusted environments, sensors may potentially be compromised. Existing approaches for securely answering aggregation queries in untrusted sensor networks can detect whether the aggregation result is corrupted by an attacker. However, the attacker (controlling the compromised sensors) can keep corrupting the result, rendering the system unavailable. This paper aims to enable aggregation queries to tolerate instead of just detecting the adversary. To this end, we propose a novel tree sampling algorithm that directly uses sampling to answer aggregation queries. It leverages a set sampling protocol to overcome a well-known obstacle in sampling-traditional sampling technique is only effective when the predicate count or sum is large. Set sampling can efficiently sample a set of sensors together, and determine whether any sensor in the set satisfies the predicate (but not how many). With set sampling as a building block, tree sampling can provably generate a correct answer despite adversarial interference, while without the drawbacks of traditional sampling techniques.

关键词： Aggregation queries Secure aggregation queries sampling algorithms Set sampling Tree sampling

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sampling Exponentially Tilted Stable Distributions

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ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2011年第1期22卷 3-3页

作者： Hofert, Marius Swiss Fed Inst Technol Dept Math CH-8092 Zurich Switzerland

Several algorithms for sampling exponentially tilted positive stable distributions have recently been suggested. Three of them are known as exact methods, that is, neither do they rely on approximations nor on numerically critical procedures. One of these algorithms is outperformed by another one uniformly over all parameters. The remaining two algorithms are based on different ideas and both have their advantages. After a brief overview of sampling algorithms for exponentially tilted positive stable distributions, the two algorithms are compared. A rule is derived when to apply which for sampling these distributions.

关键词： Stable distributions exponentially tilted stable distributions Laplace-Stieltjes transforms sampling algorithms

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General framework for the rotation of units in repeated survey sampling

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STATISTICA NEERLANDICA 2009年第3期63卷 269-293页

作者： Nedyalkova, Desislava Qualite, Lionel Tille, Yves Univ Neuchatel Inst Stat CH-2000 Neuchatel Switzerland

Coordination of probabilistic samples is a challenging theoretical problem faced by statistical institutes. One of their aims is to obtain good estimates for each wave while spreading the response burden across the entire population. There is a collection of existing solutions that try to attend to these needs. These solutions, which were developed independently, are integrated in a general framework and their corresponding longitudinal designs are computed. The properties of these longitudinal designs are discussed. It is also noted that there is an antagonism between a good rotation and control over the cross-sectional sampling design. A compromise needs to be reached between the quality of the sample coordination, which appears to be optimal for a systematic longitudinal sampling design, and the freedom of choice of the cross-sectional design. In order to reach such a compromise, an algorithm that uses a new method of longitudinal sampling is proposed.

关键词： sampling algorithms sample coordination business surveys systematic sampling

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Secure and Highly-Available Aggregation Queries in Large-Scale Sensor Networks via Set sampling

Secure and Highly-Available Aggregation Queries in Large-Sca...

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8th International Symposium on Information Processing Sensor Networks

作者： Yu, Haifeng Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781424451081

Wireless sensor networks are often queried for aggregates such as predicate count, sum, and average. In untrusted environments, sensors may potentially be compromised. Existing approaches for securely answering aggregation queries in untrusted sensor networks can detect whether the aggregation result is corrupted by an attacker. However, the. attacker (controlling the compromised. sensors) can keep corrupting the result, rendering the system unavailable. This paper aims to enable aggregation. queries to tolerate instead of just detecting the adversary. To this end;we propose a novel tree sampling algorithm that directly uses sampling to answer aggregation queries. It leverages a novel set sampling technique to overcome a key and well-known obstacle in sampling - traditional sampling technique is only effective when the predicate count or sum is large. :Set sampling can efficiently sample a set of sensors together, and determine whether any sensor in the set satisfies the predicate (but not how many). With set sampling as a building block, tree sampling can provably generate a correct answer despite adversarial interference, while without the drawbacks of traditional sampling techniques.

关键词： Aggregation queries secure aggregation queries sampling algorithms set sampling tree sampling

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The trichotomy of HAVING queries on a probabilistic database

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VLDB JOURNAL 2009年第5期18卷 1091-1116页

作者： Re, Christopher Suciu, Dan Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

We study the evaluation of positive conjunctive queries with Boolean aggregate tests (similar to HAVING in SQL) on probabilistic databases. More precisely, we study conjunctive queries with predicate aggregates on probabilistic databases where the aggregation function is one of MIN, MAX, EXISTS, COUNT, SUM, AVG, or COUNT(DISTINCT) and the comparison function is one of =, not equal, >=, >, <=, or <. The complexity of evaluating a HAVING query depends on the aggregation function, alpha, and the comparison function, theta. In this paper, we establish a set of trichotomy results for conjunctive queries with HAVING predicates parametrized by (alpha, theta). For such queries (without self-joins), one of the following three statements is true: (1) the exact evaluation problem has P-time data complexity. In this case, we call the query safe. (2) The exact evaluation problem is #P-hard, but the approximate evaluation problem has (randomized) P-time data complexity. More precisely, there exists an FPTRAS for the query. In this case, we call the query apx-safe. (3) The exact evaluation problem is #P-hard, and the approximate evaluation problem is also hard. We call these queries hazardous. The precise definition of each class depends on the aggregate considered and the comparison function. Thus, we have queries that are (MAX, >=)-safe, (COUNT, <=)-apx- safe, (SUM, =)-h azardous, etc. Our trichotomy result is a significant extension of a previous dichotomy result for Boolean conjunctive queries into safe and not safe. For each of the three classes we present novel techniques. For safe queries, wedescribe an evaluation algorithm that uses random variables over semirings. For apx-safe queries, we describe an FPTRAS that relies on a novel algorithm for generating a random possible world satisfying a given condition. Finally, for hazardous queries we give novel proofs of hardness of approximation. The results for safe queries were previously announced (in Re, C., Suciu, D. Efficien

关键词： Probabilistic databases Query evaluation sampling algorithms Semirings Safe plans

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Recent advances in inferential solutions to inverse problems

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2008年第6期16卷 797-810页

作者： Fox, Colin Univ Otago Dept Phys Dunedin New Zealand

Inferential solutions to inverse problems provide substantial advantages over deterministic methods, such as: quantitative estimates with posterior (data-dependent) error estimates, predictive densities, model comparison and direct support for optimal decisions. The ability to include arbitrary forward maps, and hence use high-level representations of the unknowns, allows structure-preserving model reduction and also allows 'classification' to be performed within the 'imaging' step. Since inferential methods make (provably) optimal use of data, the ability to reduce data to a minimal set gives cost savings in applications where collecting data is expensive. The price of these advantages is presently the relatively high computational cost of sampling algorithms for computing estimates. Hence the most significant advances are in computational methods for sample-based inference in inverse problems. In this article we review the inferential formulation of inverse problems, some reasons why it is necessary to take on the extra machinery of inferential solutions, the 'basement level' methods for computing inferential solutions, and summarize some recent advances in computational methods for inferential solutions to inverse problems.

关键词： inverse problems Bayesian inference Metropolis Hastings sampling algorithms computational cost

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Recent advances in inferential solutions to inverse problems

Recent advances in inferential solutions to inverse problems

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2nd Symposium on Inverse Problems, Design and Optimization

作者： Fox, Colin Univ Otago Dept Phys Dunedin New Zealand

关键词： inverse problems Bayesian inference Metropolis Hastings sampling algorithms computational cost

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Path sampling with stochastic dynamics: Some new algorithms

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JOURNAL OF COMPUTATIONAL PHYSICS 2007年第1期225卷 491-508页

作者： Stoltz, Gabriel Ecole Natl Ponts & Chaussees CERMICS F-77455 Champs Sur Marne France CEA DAM Ile France F-91680 Bruyeres Le Chatel France

We propose here some new sampling algorithms for path sampling in the case when stochastic dynamics are used. In particular, we present a new proposal function for equilibrium sampling of paths with a Monte-Carlo dynamics (the so called "brownian tube" proposal). This proposal is based on the continuity of the dynamics with respect to the random forcing, and generalizes all previous approaches when stochastic dynamics are used. The efficiency of this proposal is demonstrated using some measure of decorrelation in path space. We also discuss a switching strategy that allows to transform ensemble of paths at a finite rate while remaining at equilibrium, in contrast with the usual Jarzynski like switching. This switching is very interesting to sample constrained paths starting from unconstrained paths, or to perform simulated annealing in a rigorous way. (c) 2006 Elsevier Inc. All rights reserved.

关键词： molecular dynamics path sampling sampling algorithms free energy computations

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A Bayesian approach to bandwidth selection for multivariate kernel density estimation

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2006年第11期50卷 3009-3031页

作者： Zhang, Xibin King, Maxwell L. Hyndman, Rob J. Monash Univ Dept Econometr & Business Stat Clayton Vic 3800 Australia

Kernel density estimation for multivariate data is an important technique that has a wide range of applications. However, it has received significantly less attention than its univariate counter-part. The lower level of interest in multivariate kernel density estimation is mainly due to the increased difficulty in deriving an optimal data-driven bandwidth as the dimension of the data increases. We provide Markov chain Monte Carlo (MCMC) algorithms for estimating optimal bandwidth matrices for multivariate kernel density estimation. Our approach is based on treating the elements of the bandwidth matrix as parameters whose posterior density can be obtained through the likelihood cross-validation criterion. Numerical studies for bivariate data show that the MCMC algorithm generally performs better than the plug-in algorithm under the Kullback-Leibler information criterion, and is as good as the plug-in algorithm under the mean integrated squared error (MISE) criterion. Numerical studies for five-dimensional data show that our algorithm is superior to the normal reference rule. Our MCMC algorithm is the first data-driven bandwidth selector for multivariate kernel density estimation that is applicable to data of any dimension. (C) 2005 Elsevier B.V. All rights reserved.

关键词： cross-validation Kullback-Leibler information mean integrated squared errors sampling algorithms Monte Carlo kernel likelihood

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A Markov chain Monte Carlo approach to stereovision 7th

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7th European Conference on Computer Vision (ECCV 2002)

作者： Sénégas, J Ecole Mines Ctr Geostat F-77305 Fontainebleau France Istar F-06905 Sophia Antipolis France

ISBN: (纸本)3540437460

We propose Markov chain Monte Carlo sampling methods to address uncertainty estimation in disparity computation. We consider this problem at a postprocessing stage, i.e. once the disparity map has been computed, and suppose that the only information available is the stereoscopic pair. The method, which consists of sampling from the posterior distribution given the stereoscopic pair, allows the prediction of large errors which occur with low probability, and accounts for spatial correlations. The model we use is oriented towards an application to mid-resolution stereo systems, but we give insights on how it can be extended. Moreover, we propose a new sampling algorithm relying on Markov chain theory and the use of importance sampling to speed up the computation. The efficiency of the algorithm is demonstrated, and we illustrate our method with the computation of confidence intervals and-probability maps of large errors, which may be applied to optimize a trajectory in a three dimensional environment.

关键词： stereoscopic vision digital terrain models disparity uncertainty estimation sampling algorithms Bayesian computation inverse problems

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