Privacy
18 notes, newest first. Includes the vault tags privacy, federated learning.
nanoMuse: An Open-Source Personal Agent for Every Device You Own
The preprint presents nanoMuse as an open-source counterpart to persistent personal agents that operate across a user's accounts and devices. It describes a shared conversation over a user-runnable relay, a Sentinel…
AgentPrivArena: Evaluating and Auditing Real-world AI Agent Privacy
The preprint introduces AgentPrivArena, a reproducible environment that integrates authentic MCP tools and self-hosted services to evaluate privacy risks during realistic multi-step agent execution. It proposes…
Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints
The preprint proposes the Homomorphic Advantage Operator to stabilize deep reinforcement learning when nonlinear operations are approximated under fully homomorphic encryption. It applies a zero-mean centering…
PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations
PrivDrift is a benchmark for testing whether secrets disclosed during an active conversation remain recoverable after the dialogue shifts to unrelated topics and a later probe uses persuasion. It contains 1,000…
Et Tu, Brute? Economic Misalignment in Personal AI Agents
The preprint studies personal agents making economic recommendations from user context, including email and structured personal attributes. It reports 325,000 experiments across 13 agents and three decision types…
An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
The authors propose a lightweight approach to Federated Continual Learning (FCL) called FedCurv-DR for supporting artificial intelligence in cultural heritage. This method is designed to learn from distributed and…
Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
Researchers have developed Verifiable Latent Alignments (VLA), a framework for monitoring and controlling communication between language-model agents that occurs through hidden states not visible in public transcripts…
Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach
Researchers propose a new method for fine-tuning large language models in a federated learning paradigm using Low-rank adaptation (LoRA). They investigate two strategies, Share-A/Local-B and Share-B/Local-A, and reveal…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered architecture for hospital AI systems aimed at addressing the challenges of isolated AI deployments in hospitals. The proposed architecture includes an Agent Orchestration Layer, a…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered compliance-first Agentic AI architecture for hospital systems. The architecture aims to address the challenges of siloed AI deployments in hospitals by integrating various layers…
When Synthetic Speech Is All You Have: Better Call GRPO
The paper presents a new approach for adapting Large Language Model (LLM) based Automatic Speech Recognition (ASR) systems to regulated domains like banking using synthetic text-to-speech instead of real speech due to…
SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
The text introduces a new benchmark called SovereignPA-Bench for evaluating user-owned personal agents. The benchmark focuses on aspects like preserving user sovereignty, privacy, consent, and resisting manipulative…
Copewell: A Multi-Agent Swarm Architecture for Equitable Mental Wellness Support
The paper introduces Copewell, a novel multi-agent swarm system designed to improve mental health support, particularly for people in low- and middle-income countries where access is limited due to various factors. The…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Researchers introduce LACUNA, a testbed for evaluating the precision of unlearning methods in large language models (LLMs). The testbed allows direct evaluation of whether unlearning targets the weights responsible for…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Researchers introduce LACUNA, a testbed that allows for direct evaluation of unlearning methods at the parameter level. LACUNA is used to assess current state-of-the-art unlearning methods and finds them to be…
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
The paper proposes QFedAgent, a new approach for multi-agent activity recognition in federated learning. It uses a hybrid quantum-classical framework and a variational quantum circuit fusion module that models…
FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
The paper introduces FedXDS, a method that uses explainable AI (XAI) to help mitigate performance degradation in federated learning caused by statistical heterogeneity between client data distributions. The approach…
Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
The authors propose a multi-agent framework that uses semantic rewriting to enhance large language models in sensitive scenarios while preserving privacy and contextual fidelity. The framework was tested on two…
