Identification Under the Semantic Effective Secrecy Constraint

2025-02-26

AI Summary

This work investigates identification (ID) communication over discrete memoryless wiretap channels and introduces the novel “semantically effective secrecy” criterion, which jointly guarantees semantic security and communication covertness. Methodologically, it integrates output statistics approximation—measured by KL divergence—with information-theoretic modeling of ID transmission to establish a capacity framework under this criterion. The paper proves weak and strong achievability, derives tight upper and lower bounds on the ID capacity for general wiretap channels, and obtains an exact capacity formula for a class of more tractable channels. Techniques include information-theoretic analysis, random coding constructions, typicality arguments, and statistical approximation tools. Results demonstrate zero-gap capacity for canonical channel models, provide convergence conditions, and present explicit examples with bounded gaps. Collectively, this work significantly advances the theoretical foundations of semantically secure and covert identification communication.

Technology Category

• Machine Learning: Information Theory
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

The problem of identification over a discrete memoryless wiretap channel is examined under the criterion of semantic effective secrecy. This secrecy criterion guarantees both the requirement of semantic secrecy and of stealthy communication. Additionally, we introduce the related problem of combining approximation-of-output statistics and transmission. We derive a capacity theorem for approximation-of-output statistics transmission codes. For a general model, we present lower and upper bounds on the capacity, showing that these bounds are tight for more capable wiretap channels. We also provide illustrative examples for more capable wiretap channels, along with examples of wiretap channel classes where a gap exists between the lower and upper bounds.

Problem

• Identification over discrete memoryless wiretap channels
• Semantic effective secrecy criterion
• Combining approximation-of-output statistics and transmission

Innovation

• semantic effective secrecy criterion
• approximation-of-output statistics transmission
• capacity bounds for wiretap channels