Embedding edit distance to enable private keyword search

Abstract

Background: Our work is focused on fuzzy keyword search over encrypted data in Cloud Computing. Methods: We adapt results on private identification schemes by Bringer et al. to this new context. We here exploit a classical embedding of the edit distance into the Hamming distance. Results: Our way of doing enables some flexibility on the tolerated edit distance when looking for close keywords while preserving the confidentiality of the queries. Conclusion: Our proposal is proved secure in a security model taking into account privacy.