3 papers
cs.CR2026
Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading
Jianming Tong, Hanshen Xiao, Krishna Kumar Nair +5
Multi-user virtual reality enables immersive interaction. However, rendering avatars for numerous participants on each headset incurs prohibitive computational overhead, limiting s…
cs.CR2026
Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks
Chong Xiang, Drew Zagieboylo, Shaona Ghosh +5
AI agents, predominantly powered by large language models (LLMs), are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigge…
cs.CL2026
Privasis: Synthesizing the Largest "Public" Private Dataset from Scratch
Hyunwoo Kim, Niloofar Mireshghallah, Michael Duan +11
Research involving privacy-sensitive data has always been constrained by data scarcity, standing in sharp contrast to other areas that have benefited from data scaling. This challe…