Embedding edit distance to enable private keyword search
Julien Bringer, Hervé Chabanne · Human-centric Computing and Information Sciences · 2012
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.