Private Semantic Communications with Separate Blind Encoders

TL;DR

This paper introduces a privacy-preserving semantic communication framework with separate encoders, balancing privacy and utility by leveraging advanced information-theoretic methods to produce disclosed data without revealing private information.

Contribution

It proposes a novel semantic communication scheme with separate encoders that use functional representation lemmas to optimize privacy-utility trade-offs.

Findings

Effective privacy-utility trade-off achieved

New bounds derived using functional representation lemmas

Numerical example demonstrates improved performance

Abstract

We study a semantic communication problem with a privacy constraint where an encoder consists of two separate parts, e.g., encoder 1 and encoder 2. The first encoder has access to information source $X=(X_1,\ldots,X_N)$ which is arbitrarily correlated with private data $S$. The private data is not accessible by encoder 1, however, the second encoder has access to it and the output of encoder 1. A user asks for a task $h(X)$ and the first encoder designs the semantic of the information source $f(X)$ to disclose. Due to the privacy constraints $f(X)$ can not be revealed directly to the user and the second encoder applies a statistical privacy mechanism to produce disclosed data $U$. Here, we assume that encoder 2 has no access to the task and the design of the disclosed data is based on the semantic and the private data. In this work, we propose a novel approach where $U$ is produced by solving a privacy-utility trade-off based on the semantic and the private data. We design $U$ utilizing different methods such as using extended versions of the Functional Representation Lemma and the Strong Functional Representation Lemma. We evaluate our design by computing the utility attained by the user. Finally, we study and compare the obtained bounds in a numerical example.