Embedding Edit Distance to Allow Private Keyword Search in Cloud Computing

Abstract

Recently, Li et al. introduced a fuzzy keyword search over encrypted data in Cloud Computing. Their approach relies on fuzzy keyword sets which are used by a symmetric searchable encryption protocol. The idea behind these fuzzy keyword sets is to index – before the search phase – the exact keywords but also the ones differing slightly according to a fixed bound on the tolerated edit distance. We here suggest a different construction. We exploit a classical embedding of the edit distance into the Hamming distance. This enables us to adapt results on private identification schemes to this new context. This way of doing implies more flexibility on the tolerated edit distance.