1 citations · 1 across the 6 of their papers we have counts for
7 papers
Can Prompt Anonymity Protect Your Identity From LLM Providers?
Dzung Pham, Dillon Sheils, Naina Singh +1
User conversations with large language models (LLMs) often contain highly sensitive personal information that can be exploited by LLM providers to create detailed user dossiers, en…
AI Snitches Get Glitches: Towards Evading Agentic Surveillance
Hyejun Jeong, Dzung Pham, Amir Houmansadr +1
AI agents are now routinely entrusted with access to users' data and communications, operating with growing autonomy and low human supervision. This increasing reliance on AI agent…
AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
Dzung Pham, Kleomenis Katevas, Ali Shahin Shamsabadi +1
Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based question answering. While remote,…
Frankentext: Stitching random text fragments into long-form narratives
Chau Minh Pham, Jenna Russell, Dzung Pham +1
We introduce Frankentexts, a long-form narrative generation paradigm that treats an LLM as a composer of existing texts rather than as an author. Given a writing prompt and thousan…
Can Large Language Models Really Recognize Your Name?
Dzung Pham, Peter Kairouz, Niloofar Mireshghallah +3
Large language models (LLMs) are increasingly being used in privacy pipelines to detect and remedy sensitive data leakage. These solutions often rely on the premise that LLMs can r…
ProxyGPT: Enabling User Anonymity in LLM Chatbots via (Un)Trustworthy Volunteer Proxies
Dzung Pham, Jade Sheffey, Chau Minh Pham +1
Popular large language model (LLM) chatbots such as ChatGPT and Claude require users to create an account with an email or a phone number before allowing full access to their servi…
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation
Dzung Pham, Shreyas Kulkarni, Amir Houmansadr
Federated learning has emerged as a promising privacy-preserving solution for machine learning domains that rely on user interactions, particularly recommender systems and online l…