arXiv:2606. 28479v1 Announce Type: cross Abstract: CSIRTs increasingly fine tune language models on vulnerability scan records, but these records expose internal network topology and create privacy risks under regulations such as GDPR and LGPD.
By Cristhian Kapelinski, Diego Kreutz
The paper introduces Veil-then-Reveal (VtR), a user-side framework that pseudo‑anonymizes personally identifiable information (PII) in prompts sent to large language models (LLMs) under a zero‑trust model. VtR replaces detected PII with realistic, same‑type surrogates using format‑preserving encryption for structured data and a keyed‑hash dictionary for semantic data, while preserving linguistic and structural cues. The system achieves high detection recall (97.9%), strong protection of detected values (95.2%), and near‑perfect round‑trip restoration (99.9%) across three datasets.
By Mohamed Shaaban, Mohamed Elmahallawy, Thomas E. Carroll
The paper systematically studies how anonymizing input data affects large language models (LLMs). Five prominent LLMs were evaluated on eleven benchmarks, comparing performance on original versus pseudonymized inputs. Results show that anonymization generally degrades performance, with larger drops for more capable models and task-dependent effects; reversible anonymization preserves entity uniqueness better than irreversible redaction, and prompting about anonymization offers no benefit.
By Tobias Deu{\ss}er, Max Hahnb\"uck, Lorenz Sparrenberg, Tobias Uelwer, Christian Bauckhage, Rafet Sifa
FedSumUp introduces a learnable lossy compressor that generates compact, visually interpretable representations on client devices, reducing privacy leakage and computational overhead. The compressor is trained on the server using a data‑free closed‑loop optimization that minimizes the difference between original and twice‑compressed representations after upsampling. Experiments on BloodMNIST, CIFAR‑10, and ChestMNIST show low reconstruction PSNR, significant client runtime reductions, and competitive classification accuracy while remaining robust to non‑IID data.
By Jingwei Zhao, Tiantong Sun, Yuanyuan Song, Jinhuan Guo, Wei Zishuo, Shengyu Huang, Ziyuan Ren, Wei Chen, Xiaojin Zhang
Spruce is a system that enables scalable private outsourced retrieval by learning compact binary embeddings and using efficient Hamming-distance computation under a two‑server multi‑party computation protocol. It replaces costly corpus‑wide embedding scoring with a fixed‑radius protocol that avoids multi‑round candidate selection, and introduces private cluster pruning and a one‑core dealer to reduce computation and eliminate OT preprocessing bottlenecks. Across corpora of 383K–5.42M documents, Spruce maintains original search quality while achieving up to 31.5× higher throughput and reducing query times to a few seconds.
By Peichun Hua, Yunming Xiao
arXiv:2502.10673v2 Announce Type: replace-cross
Abstract: Retrieval-Augmented Generation (RAG) has become an effective method for enhancing large language models (LLMs) with up-to-date knowledge. How...
By Yepeng Liu, Xuandong Zhao, Dawn Song, Yuheng Bu