4 citations · 6 across the 16 of their papers we have counts for
6 papers · 1 filter
The Influence of Human-like Appearance on Expected Robot Explanations
Hana Kopecka, Jose Such
A robot's appearance is a known factor influencing user's mental model and human-robot interaction, that has not been studied in the context of its influence in expected robot expl…
Protecting Bystander Privacy via Selective Hearing in Audio LLMs
Xiao Zhan, Guangzhi Sun, Jose Such +1
Audio Large language models (LLMs) are increasingly deployed in the real world, where they inevitably capture speech from unintended nearby bystanders, raising privacy risks that e…
Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information
Xiao Zhan, Juan Carlos Carrillo, William Seymour +1
LLM-based Conversational AIs (CAIs), also known as GenAI chatbots, like ChatGPT, are increasingly used across various domains, but they pose privacy risks, as users may disclose pe…
Privacy Perspectives and Practices of Chinese Smart Home Product Teams
Shijing He, Yaxiong Lei, Xiao Zhan +4
Previous research has explored the privacy needs and concerns of device owners, primary users, and different bystander groups with regard to smart home devices like security camera…
Exploring the Privacy and Security Challenges Faced by Migrant Domestic Workers in Chinese Smart Homes
Shijing He, Xiao Zhan, Yaxiong Lei +3
The growing use of smart home devices poses considerable privacy and security challenges, especially for individuals like migrant domestic workers (MDWs) who may be surveilled by t…
CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models
Guangzhi Sun, Xiao Zhan, Shutong Feng +2
Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption. Current LLM safety benchmarks often focus solely on the refu…