Cancellable speech template via random binary orthogonal matrices projection hashing

The increasing advancement of mobile technology explosively popularizes the mobile devices (e.g. iPhone, iPad). A large number of mobile devices provide great convenience and cost effectiveness for the speaker recognition based applications. However, the compromise of speech template stored in mobile devices highly likely lead to the severe security and privacy breaches while the existing proposals for speech template protection do not completely guarantee the required properties such as unlinkability and non-invertibility. In this paper, we propose a cancellable transform, namely Random Binary Orthogonal Matrices Projection (RBOMP) hashing, to protect a well-known speech representation (i.e. i-vector). RBOMP hashing is inspired from Winner-Takes-All hash and further strengthened by the integration of the prime factorization (PF) function. Briefly, RBOMP hashing projects the i-vector using random binary orthogonal matrices and records the discrete value. Due to the strong non-linearity of RBOMP, the resultant hashed code withstands the template invertibility attack. Further, the experimental results suggest that the speech template generated using RBOMP hashing can still be verified with reasonable accuracy. Besides that, rigorous analysis shows that the proposed cancellable technique for speech resists several major attacks while the other criteria of biometric template protection can be justified simultaneously.

Duke Scholars

Author Ming Li DKU Faculty