Randomized Distributed Function Computation (RDFC): Ultra-Efficient Semantic Communication Applications to Privacy
Onur G\"unl\"u

TL;DR
This paper introduces the RDFC framework for privacy-preserving semantic communication, demonstrating significant reductions in communication rates and strong privacy guarantees even without shared randomness.
Contribution
It formulates RDFC as a semantic communication model with privacy constraints, providing lower bounds, numerical evaluation methods, and empirical results showing efficiency gains.
Findings
Shared randomness reduces communication rate by up to 100x.
RDFC outperforms lossless transmission in efficiency.
Privacy guarantees improve exponentially with input length.
Abstract
We establish the randomized distributed function computation (RDFC) framework, in which a sender transmits just enough information for a receiver to generate a randomized function of the input data. Describing RDFC as a form of semantic communication, which can be essentially seen as a generalized remote-source-coding problem, we show that security and privacy constraints naturally fit this model, as they generally require a randomization step. Using strong coordination metrics, we ensure (local differential) privacy for every input sequence and prove that such guarantees can be met even when no common randomness is shared between the transmitter and receiver. This work provides lower bounds on Wyner's common information (WCI), which is the communication cost when common randomness is absent, and proposes numerical techniques to evaluate the other corner point of the RDFC rate region…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques · Cryptography and Data Security
