460 research outputs found
Near-Optimal Algorithms for Differentially-Private Principal Components
Principal components analysis (PCA) is a standard tool for identifying good
low-dimensional approximations to data in high dimension. Many data sets of
interest contain private or sensitive information about individuals. Algorithms
which operate on such data should be sensitive to the privacy risks in
publishing their outputs. Differential privacy is a framework for developing
tradeoffs between privacy and the utility of these outputs. In this paper we
investigate the theory and empirical performance of differentially private
approximations to PCA and propose a new method which explicitly optimizes the
utility of the output. We show that the sample complexity of the proposed
method differs from the existing procedure in the scaling with the data
dimension, and that our method is nearly optimal in terms of this scaling. We
furthermore illustrate our results, showing that on real data there is a large
performance gap between the existing method and our method.Comment: 37 pages, 8 figures; final version to appear in the Journal of
Machine Learning Research, preliminary version was at NIPS 201
Event Stream Processing with Multiple Threads
Current runtime verification tools seldom make use of multi-threading to
speed up the evaluation of a property on a large event trace. In this paper, we
present an extension to the BeepBeep 3 event stream engine that allows the use
of multiple threads during the evaluation of a query. Various parallelization
strategies are presented and described on simple examples. The implementation
of these strategies is then evaluated empirically on a sample of problems.
Compared to the previous, single-threaded version of the BeepBeep engine, the
allocation of just a few threads to specific portions of a query provides
dramatic improvement in terms of running time
An Optimal Algorithm for Sliding Window Order Statistics
Assume there is a data stream of elements and a window of size m. Sliding window algorithms compute various statistic functions over the last m elements of the data stream seen so far. The time complexity of a sliding window algorithm is measured as the time required to output an updated statistic function value every time a new element is read. For example, it is well known that computing the sliding window maximum/minimum has time complexity O(1) while computing the sliding window median has time complexity O(log m). In this paper we close the gap between these two cases by (1) presenting an algorithm for computing the sliding window k-th smallest element in O(log k) time and (2) prove that this time complexity is optimal
Self-supervised automated wrapper generation for weblog data extraction
Data extraction from the web is notoriously hard. Of the types of resources available on the web, weblogs are becoming increasingly important due to the continued growth of the blogosphere, but remain poorly explored. Past approaches to data extraction from weblogs have often involved manual intervention and suffer from low scalability. This paper proposes a fully automated information extraction methodology based on the use of web feeds and processing of HTML. The approach includes a model for generating a wrapper that exploits web feeds for deriving a set of extraction rules automatically. Instead of performing a pairwise comparison between posts, the model matches the values of the web feeds against their corresponding HTML elements retrieved from multiple weblog posts. It adopts a probabilistic approach for deriving a set of rules and automating the process of wrapper generation. An evaluation of the model is conducted on a dataset of 2,393 posts and the results (92% accuracy) show that the proposed technique enables robust extraction of weblog properties and can be applied across the blogosphere for applications such as improved information retrieval and more robust web preservation initiatives
The state of peer-to-peer network simulators
Networking research often relies on simulation in order to test and evaluate new ideas. An important requirement of this process is that results must be reproducible so that other researchers can replicate, validate and extend existing work. We look at the landscape of simulators for research in peer-to-peer (P2P) networks by conducting a survey of a combined total of over 280 papers from before and after 2007 (the year of the last survey in this area), and comment on the large quantity of research using bespoke, closed-source simulators. We propose a set of criteria that P2P simulators should meet, and poll the P2P research community for their agreement. We aim to drive the community towards performing their experiments on simulators that allow for others to validate their results
Differentially Private Empirical Risk Minimization
Privacy-preserving machine learning algorithms are crucial for the
increasingly common setting in which personal data, such as medical or
financial records, are analyzed. We provide general techniques to produce
privacy-preserving approximations of classifiers learned via (regularized)
empirical risk minimization (ERM). These algorithms are private under the
-differential privacy definition due to Dwork et al. (2006). First we
apply the output perturbation ideas of Dwork et al. (2006), to ERM
classification. Then we propose a new method, objective perturbation, for
privacy-preserving machine learning algorithm design. This method entails
perturbing the objective function before optimizing over classifiers. If the
loss and regularizer satisfy certain convexity and differentiability criteria,
we prove theoretical results showing that our algorithms preserve privacy, and
provide generalization bounds for linear and nonlinear kernels. We further
present a privacy-preserving technique for tuning the parameters in general
machine learning algorithms, thereby providing end-to-end privacy guarantees
for the training process. We apply these results to produce privacy-preserving
analogues of regularized logistic regression and support vector machines. We
obtain encouraging results from evaluating their performance on real
demographic and benchmark data sets. Our results show that both theoretically
and empirically, objective perturbation is superior to the previous
state-of-the-art, output perturbation, in managing the inherent tradeoff
between privacy and learning performance.Comment: 40 pages, 7 figures, accepted to the Journal of Machine Learning
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