arXiv subjects

Yufei Tao

Publications and source records attributed to Yufei Tao.

21 records · Page 2Linked to original sources

arXiv2012

Optimal Algorithms for Crawling a Hidden Database in the Web

A hidden database refers to a dataset that an organization makes accessible on the web by allowing users to issue queries through a search interface. In other words, data acquisition from such a source is not by following static hyper-links. Instead, data are obtained by querying the interface, and reading the result page dynamically generated. This, with other facts such as the interface may answer a query only partially, has prevented hidden databases from being crawled effectively by existing search engines. This paper remedies the problem by giving algorithms to extract all the tuples from a hidden database. Our algorithms are provably efficient, namely, they accomplish the task by performing only a small number of queries, even in the worst case. We also establish theoretical results indicating that these algorithms are asymptotically optimal -- i.e., it is impossible to improve their efficiency by more than a constant factor. The derivation of our upper and lower bound results reveals significant insight into the characteristics of the underlying problem. Extensive experiments confirm the proposed techniques work very well on all the real datasets examined.

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arXiv2010

Transparent Anonymization: Thwarting Adversaries Who Know the Algorithm

Numerous generalization techniques have been proposed for privacy preserving data publishing. Most existing techniques, however, implicitly assume that the adversary knows little about the anonymization algorithm adopted by the data publisher. Consequently, they cannot guard against privacy attacks that exploit various characteristics of the anonymization mechanism. This paper provides a practical solution to the above problem. First, we propose an analytical model for evaluating disclosure risks, when an adversary knows everything in the anonymization process, except the sensitive values. Based on this model, we develop a privacy principle, transparent l-diversity, which ensures privacy protection against such powerful adversaries. We identify three algorithms that achieve transparent l-diversity, and verify their effectiveness and efficiency through extensive experiments with real data.

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arXiv2009

The Hardness and Approximation Algorithms for L-Diversity

The existing solutions to privacy preserving publication can be classified into the theoretical and heuristic categories. The former guarantees provably low information loss, whereas the latter incurs gigantic loss in the worst case, but is shown empirically to perform well on many real inputs. While numerous heuristic algorithms have been developed to satisfy advanced privacy principles such as l-diversity, t-closeness, etc., the theoretical category is currently limited to k-anonymity which is the earliest principle known to have severe vulnerability to privacy attacks. Motivated by this, we present the first theoretical study on l-diversity, a popular principle that is widely adopted in the literature. First, we show that optimal l-diverse generalization is NP-hard even when there are only 3 distinct sensitive values in the microdata. Then, an (l*d)-approximation algorithm is developed, where d is the dimensionality of the underlying dataset. This is the first known algorithm with a non-trivial bound on information loss. Extensive experiments with real datasets validate the effectiveness and efficiency of proposed solution.

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