Privacy-Preserving Important Passage Retrieval
Luís Marujo, José Portêlo, David Martins de Matos, João Paulo da Silva Neto, Anatole V. Gershman, Jaime Carbonell, Isabel M. Trancoso, Bhiksha Raj · arXiv (Cornell University) · 2014
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 known as Secure Binary Embeddings to convert a key phrase and bag-of-words representation to bit strings in a way that allows the computation of approximate distances, instead of exact ones. Experiments show that our secure system yield similar results to its non-private counterpart on both clean text and noisy speech recognized text.