87 citations · 178 across the 16 of their papers we have counts for
17 papers
AgentDoxx: Agentic Re-identification of Anonymized Text with Web Search
Jianing Wen, Tianshi Li
As Large Language Models (LLMs) gain tool use capabilities such as web search, they can retrieve and cross-reference public information, creating privacy risks beyond memorization.…
CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment
Bingcan Guo, Eryue Xu, Jijie Zhou +2
Aligning large language models (LLMs) with human privacy preferences requires capturing individuals' disclosure boundaries beyond general privacy norms. However, a gap remains in e…
LLM Anonymization Against Agentic Re-Identification
Ziwen Li, Jianing Wen, Tianshi Li
Agentic LLMs with web search change the threat model for text anonymization: weak contextual cues can become cross-referenceable evidence for re-identification, yet those same deta…
Autonomy Reshapes How Personalization Affects Privacy Concerns and Trust in LLM Agents
Zhiping Zhang, Yi Evie Zhang, Freda Shi +1
LLM agents require personal information for personalization in order to effectively act on users' behalf, but this raises privacy concerns that can discourage data sharing, limitin…
PrivacyMotiv: Vulnerability-Centered Persona Journeys for Empathic Privacy Reviews in UX Design
Zeya Chen, Jianing Wen, Yaxing Yao +2
UX professionals routinely conduct design reviews, yet privacy concerns are often overlooked, not only due to limited tools, but more fundamentally from low intrinsic motivation, d…
"I'm categorizing LLM as a productivity tool": Examining ethics of LLM use in HCI research practices
Shivani Kapania, Ruiyi Wang, Toby Jia-Jun Li +2
Large language models are increasingly applied in real-world scenarios, including research and education. These models, however, come with well-known ethical issues, which may mani…
{A New Hope}: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation
Shidong Pan, Zhen Tao, Thong Hoang +7
Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, th…
Matcha: An IDE Plugin for Creating Accurate Privacy Nutrition Labels
Tianshi Li, Lorrie Faith Cranor, Yuvraj Agarwal +1
Apple and Google introduced their versions of privacy nutrition labels to the mobile app stores to better inform users of the apps' data practices. However, these labels are self-r…
Human-Centered Privacy Research in the Age of Large Language Models
Tianshi Li, Sauvik Das, Hao-Ping Lee +3
The emergence of large language models (LLMs), and their increased use in user-facing systems, has led to substantial privacy concerns. To date, research on these privacy concerns…
"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents
Zhiping Zhang, Michelle Jia, Hao-Ping Lee +5
The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs…
Shaping the Emerging Norms of Using Large Language Models in Social Computing Research
Hong Shen, Tianshi Li, Toby Jia-Jun Li +2
The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analy…
ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language Models
Jackie Junrui Yang, Yingtian Shi, Yuhan Zhang +7
By combining voice and touch interactions, multimodal interfaces can surpass the efficiency of either modality alone. Traditional multimodal frameworks require laborious developer…
The Design of the User Interfaces for Privacy Enhancements for Android
Jason I. Hong, Yuvraj Agarwal, Matt Fredrikson +22
We present the design and design rationale for the user interfaces for Privacy Enhancements for Android (PE for Android). These UIs are built around two core ideas, namely that dev…
Decentralized is not risk-free: Understanding public perceptions of privacy-utility trade-offs in COVID-19 contact-tracing apps
Tianshi Li, Jackie, Yang +5
Contact-tracing apps have potential benefits in helping health authorities to act swiftly to halt the spread of COVID-19. However, their effectiveness is heavily dependent on their…
Understanding the Interactions of Workloads and DRAM Types: A Comprehensive Experimental Study
Saugata Ghose, Tianshi Li, Nastaran Hajinazar +2
It has become increasingly difficult to understand the complex interaction between modern applications and main memory, composed of DRAM chips. Manufacturers are now selling and pr…
Flexible-Latency DRAM: Understanding and Exploiting Latency Variation in Modern DRAM Chips
Kevin K. Chang, Abhijith Kashyap, Hasan Hassan +7
This article summarizes key results of our work on experimental characterization and analysis of latency variation and latency-reliability trade-offs in modern DRAM chips, which wa…
Using ECC DRAM to Adaptively Increase Memory Capacity
Yixin Luo, Saugata Ghose, Tianshi Li +5
Modern DRAM modules are often equipped with hardware error correction capabilities, especially for DRAM deployed in large-scale data centers, as process technology scaling has incr…