arXiv subjects

Zhaoqian Liu

Publications and source records attributed to Zhaoqian Liu.

3 recordsLinked to original sources

arXiv2026

Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack

Semantic communication enhances transmission efficiency by conveying semantic information rather than raw input symbol sequences. Task-oriented semantic communication further aims to retain only task-specific information, thereby achieving greater bandwidth savings. However, these neural-network-based communication systems are vulnerable to model inversion attacks, in which adversaries attempt to recover sensitive input information from intercepted semantic features. The key challenge is therefore to preserve privacy while maintaining task accuracy and robustness. We consider a task-confidential setting in which the adversary attempts to reconstruct the original input from intercepted features without knowing the legitimate receiver's task or model. Although PSNR and SSIM are commonly used to assess reconstruction quality, we find that an external classifier can still perform the legitimate receiver's task with nontrivial accuracy on reconstructions with low PSNR or SSIM, indicating that these reconstructions still contain task-level semantic leakage. We therefore propose DiffSem, which splits the diffusion process between controlled transmitter-side self-noising and matched receiver-side reverse denoising. Experiments on the MNIST, CIFAR-10, and CelebA datasets show that DiffSem improves the legitimate receiver's task accuracy without increasing either the transmitted feature size or information leakage.

cs.CR↗

arXiv2026

Diffusion-Aided Bandwidth-Efficient Semantic Communication with Adaptive Requests

Semantic communication focuses on conveying the task-relevant meaning rather than exact bitwise recovery. For image transmission with a generative receiver, relying only on text descriptions can be insufficient to preserve instance-specific visual evidence, whereas sending dense latent representations can incur substantial overhead. This paper presents a receiver-driven closed-loop scheme that transmits a short caption together with an initial sparse subset of latent blocks, and then uses feedback to request additional blocks only when needed. At each round, the receiver reconstructs the image via latent diffusion inpainting and applies a semantic consistency check between a caption generated from the reconstruction and the received caption, using a lightweight language similarity score such as ROUGE-L. The receiver stops early once a target consistency level is met, and otherwise requests a small number of additional latent blocks to refine the reconstruction. Experiments on Flickr30k over AWGN channels demonstrate a controllable rate-quality tradeoff. Adaptive feedback achieves the strongest semantic alignment and the lowest failure rate, outperforming budget-matched one-shot transmission while typically using fewer latent blocks than always-on retransmission.

cs.IT↗

arXiv2025

Efficient and High-Accuracy Secure Two-Party Protocols for a Class of Functions with Real-number Inputs

In two-party secret sharing scheme, values are typically encoded as unsigned integers $\mathsf{uint}(x)$, whereas real-world applications often require computations on signed real numbers $\mathsf{Real}(x)$. To enable secure evaluation of practical functions, it is essential to computing $\mathsf{Real}(x)$ from shared inputs, as protocols take shares as input. At USENIX'25, Guo et al. proposed an efficient method for computing signed integer values $\mathsf{int}(x)$ from shares, which can be extended to compute $\mathsf{Real}(x)$. However, their approach imposes a restrictive input constraint $|x| < \frac{L}{3}$ for $x \in \mathbb{Z}_L$, limiting its applicability in real-world scenarios. In this work, we significantly relax this constraint to $|x| < B$ for any $B \leq \frac{L}{2}$, where $B = \frac{L}{2}$ corresponding to the natural representable range in $x \in \mathbb{Z}_L$. This relaxes the restrictions and enables the computation of $\mathsf{Real}(x)$ with loose or no input constraints. Building upon this foundation, we present a generalized framework for designing secure protocols for a broad class of functions, including integer division ($\lfloor \frac{x}{d} \rfloor$), trigonometric ($\sin(x)$) and exponential ($e^{-x}$) functions. Our experimental evaluation demonstrates that the proposed protocols achieve both high efficiency and high accuracy. Notably, our protocol for evaluating $e^{-x}$ reduces communication costs to approximately 31% of those in SirNN (S&P 21) and Bolt (S&P 24), with runtime speedups of up to $5.53 \times$ and $3.09 \times$, respectively. In terms of accuracy, our protocol achieves a maximum ULP error of $1.435$, compared to $2.64$ for SirNN and $8.681$ for Bolt.

cs.CR↗