arXiv Machine Learning

Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications

arXiv:2606. 31973v1 Announce Type: cross Abstract: Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw data, enabling efficient and robust communication under resource and channel constraints.

arXiv Machine Learning

Sep 25

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

The paper introduces DiffSem, a diffusion-based approach for task‑oriented semantic communications that splits the diffusion process between transmitter‑side self‑noising and receiver‑side reverse denoising. It addresses privacy concerns by reducing model‑inversion attacks while preserving task accuracy, as demonstrated on MNIST, CIFAR‑10, and CelebA datasets. The method achieves higher task performance without enlarging transmitted feature size or increasing semantic leakage.

By Xuesong Wang, Mo Li, Xingyan Shi, Zhaoqian Liu, Shenghao Yang

diffusion safety

arXiv Computation and Language

Sep 22

LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication

arXiv:2609.23193v1 Announce Type: cross Abstract: As Large Language Model (LLM) APIs become increasingly integrated into privacy-sensitive workflows, ensuring inference-time privacy without compromis...

By Yuzhu Mao, Liang Zhao

llms safety

arXiv Machine Learning

Aug 6

DeepInvert: Semi-Supervised Embedding Inversion Against Obfuscated Language Models

arXiv:2608. 04477v1 Announce Type: cross Abstract: Cloud-based language model services routinely process prompts containing sensitive information.

By Zhicong Huang, Cheng Hong, Tao Wei

llms rag

arXiv Machine Learning

Jun 30

Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel

arXiv:2606. 30595v1 Announce Type: cross Abstract: Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless channels.

By Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

arXiv AI

Jun 15

From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models

arXiv:2606. 14210v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high computational cost of local deployment.

By Zixuan Gu, Xiaojun Ye, Yang Liu

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jul 15

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.

By Rasmus Torp, Shailen K. Smith, Adam Breuer

benchmarks safety

arXiv AI

Sep 1

Auditing and Mitigating Privacy Leakage in Cloud-Edge Collaborative Decoding

arXiv:2608.29111v1 Announce Type: cross Abstract: Applications such as personalized assistance and proprietary document analysis require large language models (LLMs) to generate outputs from private...

By Kejia Zhang, Tianyuan Zou, Zixuan GU, Yang Liu

llms safety

arXiv Machine Learning

Jun 17

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

arXiv:2606. 17110v1 Announce Type: cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets.

By Md Abdullah Al Mamun, Ngoc Phu Doan, Pedram Zaree, Ihsen Alouani, Nael Abu-Ghazaleh

llms multimodal safety

arXiv Machine Learning

Sep 15

Privacy-Preserving Deep Joint Source-Channel Coding with In-Loop Concept Erasure

arXiv:2609.13393v1 Announce Type: cross Abstract: Deep joint source-channel coding (DeepJSCC) transmits learned semantic features efficiently but can leak sensitive attributes such as gender, race, o...

By Rami Eid, Maria Slim, Mariette Awad, Hadi Sarieddeen

safety

Hugging Face Trending Papers

Jun 29

Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel

Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless channels. The adoption of SemCom in shared-access wireless networks introduces new vulnerabilities for multi-user semantic inference.

arXiv AI

Jul 1

FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning

arXiv:2606. 31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users.

By Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek

safety

arXiv AI

Aug 20

Denoising-Aware Inversion: Revealing Privacy Risks in Noise-Protected Text Embeddings

The paper investigates the vulnerability of Gaussian‑noised text embeddings to inversion attacks. It identifies a "Double Noise Trap" that hampers standard generative methods and introduces DAEI, a denoising‑aware pipeline that significantly outperforms existing baselines in reconstructing original text. Experiments show DAEI improves BLEU by 154% and token‑level metrics by 32–60%.

By Yubo Wang, Shujie Cui, James Bailey, Hongzhi Yin, Wenyu Liang, Min Tang, Shiyue Qin, Weiqing Wang

rag safety

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