Rényi Rate-Distortion-Perception-Privacy Tradeoff under Indirect Observation
This work provides a theoretical framework for balancing rate, distortion, perception, and privacy in indirect source coding, relevant to information theorists and privacy researchers.
The paper introduces a Rényi Rate-Distortion-Perception-Privacy (R-RDPP) framework for indirect source coding, characterizing the scalar Gaussian tradeoff and showing that standard privacy metrics penalize legitimate semantic recovery. It proposes a conditional privacy measure and refines achievability bounds using Poisson functional representation, yielding exact closed-form expressions for integer-order Rényi entropies.
We introduce a Rényi Rate-Distortion-Perception-Privacy (R-RDPP) framework for indirect source coding. A latent source~$S$ is correlated with a private attribute~$U$, and the encoder observes only a noisy view~$X$ such that $(S,U) - X - Y$ holds at the decoder output~$Y$. The communication cost is measured by Sibson's $α$-mutual information $\Ialp$, the privacy leakage by $\Ibeta$, the semantic distortion between $S$ and $Y$, and the realism constraint at the semantic marginal $P_S$. We characterize the scalar Gaussian RDPP tradeoff, revealing that standard privacy metrics inherently penalize legitimate semantic recovery. To resolve this, we introduce a conditional privacy measure that quantifies only the residual leakage. In addition, we refine the achievability bounds for $α> 1$ via the Poisson functional representation. By deriving the exact geometric-mixture distribution of the Poisson index, we obtain exact closed-form expressions for integer-order Rényi entropies and sharper computable bounds in regimes where the resulting expression improves the logarithmic-moment approach.