Secret Key-based Authentication With Passive Eavesdropper for Scalar Gaussian Sources
This addresses authentication security for Gaussian data sources in communication systems, though it appears incremental as it extends known discrete-source results to Gaussian cases.
The paper analyzes secret key-based authentication systems with eavesdroppers for correlated Gaussian sources, characterizing trade-offs among secret-key, storage, and privacy-leakage rates. It reveals that Gaussian cases do not require a second auxiliary random variable (unlike discrete sources) and achieves strong secrecy with computable capacity region expressions.
We analyze the fundamental trade-off of secret key-based authentication systems in the presence of an eavesdropper for correlated Gaussian sources. A complete characterization of trade-off among secret-key, storage, and privacy-leakage rates of both generated and chosen secret models is provided. One of the main contributions is revealing that unlike the known results for discrete sources, there is no need for the second auxiliary random variable in characterizing the capacity regions for the Gaussian cases. In addition, it is shown that the strong secrecy for secrecy-leakage of the systems can be achieved by an information-spectrum approach, and the parametric expressions (computable forms) of the capacity regions are also derived.