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New system MemPrivacy shields user data in edge-cloud AI agents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MemPrivacy, a system designed to protect sensitive user information in LLM-powered agents that utilize cloud-assisted memory management. MemPrivacy identifies and masks private data on edge devices with structured placeholders before sending it to the cloud for processing, then restores the original values locally. This approach aims to maintain memory utility and personalization quality while significantly reducing sensitive data exposure, outperforming existing models like GPT-5.2 and Gemini-3.1-Pro in privacy extraction and reducing latency. AI

IMPACT Enhances privacy for AI agents, potentially enabling wider adoption in sensitive applications by minimizing data exposure.

RANK_REASON The cluster contains a new academic paper detailing a novel system and benchmark for privacy in AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New system MemPrivacy shields user data in edge-cloud AI agents

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The cluster contains a new academic paper detailing a novel system and benchmark for privacy in AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zhiyu Li ·

    MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents

    As LLM-powered agents are increasingly deployed in edge-cloud environments, personalized memory has become a key enabler of long-term adaptation and user-centric interaction. However, cloud-assisted memory management exposes sensitive user information, while existing privacy prot…

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