Exploring a Few Good Tuples From a Text Database

Information extraction from text databases is a useful paradigm to populate relational tables and unlock the considerable value hidden in plain-text documents. However, information extraction can be expensive, due to various complex text processing steps necessary in uncovering the hidden data. There are a large number of text databases available, and not every text database is necessarily relevant to every relation. Hence, it is important to be able to quickly explore the utility of running an extractor for a specific relation over a given text database before carrying out the expensive extraction task. In this paper, we present a novel exploration methodology of finding a few good tuples for a relation that can be extracted from a database which allows for judging the relevance of the database for the relation. Specifically, we propose the notion of a good(k,l) query as one that can return any k tuples for a relation among the top-l fraction of tuples ranked by their aggregated confidence scores, provided by the extractor; if these tuples have high scores, the database can be determined as relevant to the relation. We formalize the access model for information extraction, and investigate efficient query processing algorithms for good(k,l) queries, which do not rely on any prior knowledge about the extraction task or the database. We demonstrate the viability of our algorithms using a detailed experimental study with real text databases

Accurate and Efficient Private Release of Datacubes and Contingency Tables

A central problem in releasing aggregate information about sensitive data is to do so accurately while providing a privacy guarantee on the output. Recent work focuses on the class of linear queries, which include basic counting queries, data cubes, and contingency tables. The goal is to maximize the utility of their output, while giving a rigorous privacy guarantee. Most results follow a common template: pick a "strategy" set of linear queries to apply to the data, then use the noisy answers to these queries to reconstruct the queries of interest. This entails either picking a strategy set that is hoped to be good for the queries, or performing a costly search over the space of all possible strategies. In this paper, we propose a new approach that balances accuracy and efficiency: we show how to improve the accuracy of a given query set by answering some strategy queries more accurately than others. This leads to an efficient optimal noise allocation for many popular strategies, including wavelets, hierarchies, Fourier coefficients and more. For the important case of marginal queries we show that this strictly improves on previous methods, both analytically and empirically. Our results also extend to ensuring that the returned query answers are consistent with an (unknown) data set at minimal extra cost in terms of time and noise

Differentially Private Publication of Sparse Data

The problem of privately releasing data is to provide a version of a dataset without revealing sensitive information about the individuals who contribute to the data. The model of differential privacy allows such private release while providing strong guarantees on the output. A basic mechanism achieves differential privacy by adding noise to the frequency counts in the contingency tables (or, a subset of the count data cube) derived from the dataset. However, when the dataset is sparse in its underlying space, as is the case for most multi-attribute relations, then the effect of adding noise is to vastly increase the size of the published data: it implicitly creates a huge number of dummy data points to mask the true data, making it almost impossible to work with. We present techniques to overcome this roadblock and allow efficient private release of sparse data, while maintaining the guarantees of differential privacy. Our approach is to release a compact summary of the noisy data. Generating the noisy data and then summarizing it would still be very costly, so we show how to shortcut this step, and instead directly generate the summary from the input data, without materializing the vast intermediate noisy data. We instantiate this outline for a variety of sampling and filtering methods, and show how to use the resulting summary for approximate, private, query answering. Our experimental study shows that this is an effective, practical solution, with comparable and occasionally improved utility over the costly materialization approach