A Unified Framework for Joint Semantic and Privacy Design Under Bounded Leakage
Amirreza Zamani, Sajad Daei, Abolfazl Changizi, Mikael Skoglund · 2025
We present a unified framework for semantic communication under bounded privacy leakage, in which an encoder must reveal selected information about a source while restricting the disclosure of correlated private data. In contrast to prior work that merely injects noise over a fixed representation, we co-design both the semantic mapping and the noise mechanism. Leveraging extended versions of the Functional Representation Lemma (FRL) and the Strong Functional Representation Lemma (SFRL), we model how the disclosed data arise from the original source and correlated private data. We then formulate a new optimization problem to align the resulting distributions with a “goal” distributionbalancing accuracy for the user's task and privacy constraints on sensitive data. Furthermore, we propose two distinct mapping strategies that map the original signal domain to a compact semantic space. Our numerical results verify the effectiveness of this joint design, demonstrating significant benefits over conventional methods for privacy-constrained semantic communication.