Using private documents to improve search in public documents

I’m back from the SIAM International Conference on Data Mining, and the 5th Workshop on Link Analysis, Counterterrorism, and Security, which I helped to organize. The workshop papers are now online, along with some open problems that were discussed at the end of the workshop.

I’ll post about some ideas that were tossed around at the workshop and conference in the next few days.

Let me start by talking about the work of Roger Bradford. Information retrieval starts from a document-term matrix, which is typically extremely large and sparse, and then reduces the dimensionality by using an SVD, a process sometimes called latent semantic indexing. This creates a representation space for both documents and terms. A query is treated as if it were a kind of short document and mapped into this representation space. Its near neighbours are then the documents retrieved in response to the query; and they can be sorted in decreasing distance from the query point as well.

Bradford showed that the original space can be built using a set of private documents and a set of public documents, and that the resulting representation space allows better retrieval performance than the space derived from the public documents, without allowing the properties of the private documents to be inferred.

In fact, the set of private documents can be diluted by mixing them with other documents before the process starts, making it even more difficult to work backwards to the private documents.

This process has a number of applications that he talks about in the paper. One of the most interesting is that it allows different organizations, for example allies, to share sensitive information without compromising it to each other — and still get the benefits of the relationships in the full set of documents.

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