Multi-Task Private Semantic Communication

TL;DR

This paper introduces a multi-task private semantic communication framework that optimizes information disclosure while preserving privacy, using noise addition techniques and problem decomposition.

Contribution

It extends single-task privacy methods to multi-task scenarios, deriving a simple source semantics design and demonstrating problem decomposition into parallel tasks.

Findings

Effective noise addition methods for privacy preservation.

Multi-task problem can be decomposed into parallel single-task problems.

Proposed framework improves utility while maintaining privacy constraints.

Abstract

We study a multi-task private semantic communication problem, in which an encoder has access to an information source arbitrarily correlated with some latent private data. A user has $L$ tasks with priorities. The encoder designs a message to be revealed which is called the semantic of the information source. Due to the privacy constraints the semantic can not be disclosed directly and the encoder adds noise to produce disclosed data. The goal is to design the disclosed data that maximizes the weighted sum of the utilities achieved by the user while satisfying a privacy constraint on the private data. In this work, we first consider a single-task scenario and design the added noise utilizing various methods including the extended versions of the Functional Representation Lemma, Strong Functional Representation Lemma, and separation technique. We then study the multi-task scenario and derive a simple design of the source semantics. We show that in the multi-task scenario the main problem can be divided into multiple parallel single-task problems.