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In this paper we introduce the problem of providing privacy preserving information for Web indexing, classification, and other information retrieval task. Web pages are represented by a frequency term vector that preserves k-anonymity for all the Web pages. This vector can then be used, for example, to build indexes of classifiers. Our proposal makes use of semantic micro aggregation.
We introduce a novel metric space search data structure called EGNAT, which is fully dynamic and designed for secondary memory. The EGNAT is based on Brin's GNAT static index, and partitions the space according to hyperplanes. The EGNAT implements deletions using a novel technique dubbed Ghost Hyperplanes, which is of independent interest for other metric space indexes. We show experimentally that...
Metric space searching is an emerging technique to address the problem of efficient similarity searching in many applications, including multimedia databases and other repositories handling complex objects. Although promising, the metric space approach is still immature in several aspects that are well established in traditional databases. In particular, most indexing schemes are not dynamic, that...
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