P-GTM: privacy-preserving google tri-gram method for semantic text similarity

Abstract

This paper presents P-GTM, a privacy-preserving text similarity algorithm that extends the Google Tri-gram Method (GTM). The Google Tri-gram Method is a high-performance unsupervised semantic text similarity method based on the use of context from the Google Web 1T n-gram dataset. P-GTM computes the semantic similarity between two input bag-of-words documents on public cloud hardware, without disclosing the documents' contents. Like the GTM, P-GTM requires the uni-gram and tri-gram lists from the Google Web 1T n-gram dataset as additional inputs. The need for these additional lists makes private computation of GTM text similarities a challenging problem. P-GTM uses a combination of pre-computation, encryption, and randomized preprocessing to enable private computation of text similarities using the GTM. We discuss the security of the algorithm and quantify its privacy using standard and real life corpora.