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Published on: December 15, 2023

A privacy-preserving multi-user retrieval system for multimodal artificial intelligence.

Yifang Gao¹, Wei Luo², Chuanchuan Wang³

¹School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300, Penang, Malaysia.

Scientific Reports

|February 24, 2026

Summary

This summary is machine-generated.

This study introduces PMIRS, a secure system for privacy-preserving multimodal retrieval using large language models (LLMs). PMIRS enhances data security through obfuscation, encryption, and federated learning, achieving high accuracy and low latency.

Keywords:

Cross-modal retrieval Multimodal deep learning Multimodal systems Privacy-preserving AI Searchable encryption

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Area of Science:

Artificial Intelligence
Computer Security
Information Retrieval

Background:

Large language models (LLMs) are crucial for AI but pose privacy risks in multi-user cloud environments.
Secure and private multimodal data retrieval is a significant challenge.

Purpose of the Study:

To present PMIRS, a novel system for secure, privacy-focused retrieval of multimodal image and text data.
To enable safe transmission of data to cloud-based LLMs while mitigating privacy risks.

Main Methods:

PMIRS employs obfuscation techniques, encrypted inference (AES-CBC), and federated learning to fine-tune a CLIP-based model.
The Diffie-Hellman algorithm ensures secure key management in multi-user settings.
Query embeddings are obfuscated via block-wise projection and encrypted.

Main Results:

PMIRS achieved F1-scores up to 0.92 and precision over 0.90 on a customized ImageNet dataset.
Retrieval latency was consistently below 180 milliseconds.
PMIRS improved average F1-score by 7.67% compared to the CLIP baseline, maintaining precision.

Conclusions:

PMIRS offers a practical solution for secure, efficient, and privacy-preserving multimodal retrieval.
The system has potential applications in medical imaging, customer service, and enterprise data management under regulations like GDPR and HIPAA.