This paper demonstrates that patient-facing RAG chatbots frequently expose sensitive system configurations, knowledge base details, and conversation history through client-server communication, posing…

This paper proposes a comprehensive federated learning workflow that enhances privacy and robustness by integrating personalized differential privacy budgets and client drift detection, achieving bett…

The paper introduces a Contextual Integrity (CI) framework and a new benchmark (DelegateCI-Bench) to rewrite user queries sent to cloud LLMs, ensuring only task-essential information is retained while…

The paper introduces CYBERMASKQA, a novel privacy-aware benchmark designed to evaluate Large Language Models' ability to perform accurate cybersecurity question answering while simultaneously preservi…

The paper introduces Sherpa.ai, a multi-party Private Set Union (PSU) protocol that enables privacy-preserving entity alignment for Vertical Federated Learning (VFL) without disclosing shared sample i…

The paper proposes FLRSP, a privacy-preserving federated learning method that enhances robustness by randomly selecting model parameters for global model updates, maintaining high accuracy against sta…

CivicShield introduces a novel, seven-layered defense-in-depth framework that significantly enhances the security of government-facing AI chatbots against sophisticated multi-turn adversarial attacks.

This paper systematically measured web tracking across 20 popular AI chatbots, finding that a majority share both conversational content and user identity information with third parties.

PIIGuard introduces a novel webpage-level defense mechanism using optimized hidden HTML fragments to prevent LLM assistants from scraping contact-style PII, achieving high defense success rates while…

FedAttr introduces a novel client-level attribution protocol for Federated Learning (FL) that accurately identifies which clients trained on watermarked data while maintaining strong privacy guarantee…

The paper introduces the Sovereign Context Protocol (SCP), an open-source, attribution-aware data access layer designed to standardize how Large Language Models (LLMs) connect to and track usage of hu…

The paper proposes Retrieval Augmented Classification (RAC) as a robust, low-leakage method for classifying confidential documents, demonstrating that RAC outperforms supervised fine-tuning (FT) parti…

The paper introduces TorchSight, an open-source local system using a fine-tuned Qwen 3.5 27B model that achieves high accuracy (95.0%) in classifying sensitive security documents without relying on ex…

This paper demonstrates a novel, multi-stage privacy-leakage attack chain against black-box chatbot agents by combining indirect prompt injection with web-tool invocation, showing that such attacks ar…

FedDetox introduces a robust framework that sanitizes toxic data on edge devices during federated learning to maintain the safety alignment of Small Language Models (SLMs) without sacrificing utility.

The paper introduces MyPhoneBench, a new framework that demonstrates that current phone-use agents often fail to respect user privacy, even when successfully completing simple tasks, primarily due to…

The paper introduces LLUMI, an open-source framework that improves LLM writing assistance for mental health support using community feedback, demonstrating comparable performance to proprietary models…

The paper introduces a secure Federated RAG system that enables confidential retrieval and LLM inference across distributed, private data silos.

The paper introduces the PROMPT framework to systematically analyze and mitigate privacy risks in online propaganda detection pipelines, demonstrating that current widely used methods are often non-co…

SecMate is a multi-agent virtual customer assistant for cybersecurity troubleshooting that significantly improves resolution rates (from 50% to over 90%) by integrating device, user, and service-speci…