Plausible Deniability in Fully Homomorphic Computation

TL;DR

This paper presents PD-FHC, a framework for privacy-preserving, deniable outsourced Boolean computation using RGB images and Fredkin-gate circuits, with formal privacy guarantees and practical implementation.

Contribution

It introduces a novel deniable computation scheme for fully homomorphic computation that combines image-based decoys with formal privacy proofs.

Findings

Formal privacy guarantees with advantage $ heta(1/(n-1)!)$

Implementation benchmarks show competitive performance with TFHE

Decoy-based deniability effectively prevents real computation disclosure

Abstract

We introduce \emph{Plausible Deniability in Fully Homomorphic Computation} (PD-FHC), a framework enabling users to outsource Boolean computations to an untrusted cloud while maintaining both computational privacy against honest-but-curious providers and plausible deniability against coercive adversaries. We define the notion of a \emph{Deniable Computation Medium} (DCM) and a \emph{Deniable Computation Scheme} (DCS) as medium-independent abstractions, then instantiate them using RGB images with Fredkin-gate circuits. Multiple computation scenarios (one real, several decoys) are embedded at secret positions within cover images; the cloud applies identical operations to every pixel, processing all scenarios uniformly. Under coercion, the user reveals a decoy computation with verifiable results while the real computation remains hidden. We formalize multi-round coercion games with existence and intent distinguishing advantages, proving computational privacy with advantage $\Theta(1/(n-1)!)$ and negligible existence-hiding advantage for the image instantiation. Our Python implementation, benchmarked across circuit sizes (5--289 gates) and image dimensions ($128^2$ to $512^2$), demonstrates competitive performance with TFHE for Boolean circuits while providing deniability that FHE fundamentally cannot offer.