Nonlinear Masking and Iterative Learning Decryption for Secure Communications

Typical masking techniques adopted in the conventional secure communication schemes are the additive masking and modulation by multiplication. In order to enhance security, this paper presents a nonlinear masking methodology, applicable to the conventional schemes. In the proposed cryptographic scheme, the plaintext spans over a pre-specified finite-time interval, which is modulated through parameter modulation, and masked chaotically by a nonlinear mechanism. An efficient iterative learning algorithm is exploited for decryption, and the sufficient condition for convergence is derived, by which the learning gain can be chosen. Case studies are conducted to demonstrate the effectiveness of the proposed masking method.