← Back to all news
arXiv AI By Dixi Yao, Tahseen Rabbani, Tian Li

Private Retrieval Augmented Generation via Random Projection

Read the original on arXiv AI →

The paper introduces a lightweight privacy-preserving technique for Retrieval-Augmented Generation (RAG) systems that uses random projection to transform both datastore and query embeddings. This approach maintains semantic similarity for effective retrieval while significantly reducing the risk of data extraction attacks. Experiments across various RAG architectures and datasets demonstrate that the method outperforms prior differential privacy-based techniques in both retrieval and generation performance while offering comparable empirical privacy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 7

Shadow Queries for Private Retrieval in Vector Databases

The paper introduces SHAQ, a defense called Shadow Query Generation that protects document embeddings in vector databases from embedding inversion attacks. SHAQ replaces direct embeddings with diverse shadow queries generated by a language model, thereby decomposing document semantics and decoupling stored embeddings from the original text. Experiments on various IR datasets show that SHAQ significantly lowers recovery rates, defends more tokens than baseline methods, and even improves retrieval utility.

By Xinguo Feng, Zhongkui Ma, Zihan Wang, Chuan Yan, Guowei Yang, Alsharif Abuadbba, Guangdong Bai
llmsragsafety
More like this →
arXiv AI
Sep 16

RAG-CT: Mitigating Privacy Risks on Retrieval-Augmented Generation Systems via Scanning Prompt Distribution

RAG-CT is a defense mechanism designed to protect Retrieval-Augmented Generation (RAG) systems from leaking personally identifiable information (PII). It works by detecting malicious queries through analysis of entropy and margin distributions, applying a score-based detection method. Experiments across four attack strategies and four baseline defenses on two datasets show that RAG-CT significantly reduces PII leakage while outperforming existing defenses, all without altering the underlying LLM or retriever.

By Xingyu Lyu, Jiayimei Wang, Jianfeng He, Ning Wang, Yidan Hu, Yimin Chen
llmsragbenchmarkssafety
More like this →