Secure Sparse Representations in L0 Norm Minimization and Its Application to EtC Systems
Takayuki Nakachi, Hitoshi Kiya · 2019
In this paper, we propose a method to estimate secure sparse representations in LO norm minimization and its application to Encryption-then-Compression (EtC) systems. The proposed scheme provides a practical Orthogonal Matching Pursuit (OMP) algorithm that allows computation in the encrypted domain. We prove, theoretically, that the proposal has exactly the same estimation performance as the unencrypted variant of the OMP algorithm. We demonstrate the security strength of the proposed secure sparse representations. Even if the dictionary information is leaked, the proposed scheme protects the privacy information of the observed signals.