Tianshi Li

I’m an Assistant Professor at Northeastern University in the Khoury College of Computer Sciences, directing the PEACH (Privacy-Enabling AI and Computer-Human interaction) Lab. I’m also a core faculty member at the Cybersecurity and Privacy Institute at Northeastern University.

Come and join us if you’re interested in designing and building human-centered solutions for privacy! My more recent interest focuses on studying and addressing the emerging AI privacy issues from a human-centered perspective. Read my recent papers (1, 2, 3, 4) to learn more. Check out my talk slides that summarize our lab’s recent work on the novel privacy threats and mitigation in LLM-powered interactive systems. Read my research statement to learn more about my doctoral dissertation on developer support for privacy.

I’m looking to hire PhD students starting in Fall 2025 with strong technical backgrounds and/or experiences in human-centered privacy research. Please apply to the Khoury CS PhD program and mention my name if you’re interested in working with me.

My research interests lie at the intersection of Human-Computer Interaction (HCI) and Privacy, with a focus on AI Privacy. I strive to address the increasing privacy issues in today’s digital world using a blend of human-centered problem understanding and technical problem solving. I conduct mixed-methods research to understand the privacy challenges situated in different stakeholders’ lived experiences, and build systems and conduct computational experiments to measure, model, and tackle these human-centered problems.

News

Oct 18, 2024	Our HCOMP 2024 paper “Investigating What Factors Influence Users’ Rating of Harmful Algorithmic Bias and Discrimination” won the best paper award!
Sep 26, 2024	Our PrivacyLens paper has been accepted to the NeurIPS 2024 Track on Datasets and Benchmarks. We introduce a novel framework to benchmark emerging unintended privacy leakage issues in LM agents, which also presents a method for operationalizing the contextual integrity framework with the help of LLMs. Check out our preprint and website to learn more.
Sep 20, 2024	Two papers accepted at CSCW 2025! One on secret use of LLMs, and another on ethics of LLM use in HCI research.
Sep 19, 2024	I’m grateful to have received a gift grant of $50K from Google for designing human-centered privacy protection in text input methods! I also visited the Gboard team today and gave a talk titled “Navigating Privacy in the Age of LLMs: A Human-Centered Perspective.” Looking forward to more collaborations!
Jul 17, 2024	Excited to share our NSF SaTC award on “Empathy-Based Privacy Education and Design through Synthetic Persona Data Generation” ($600K in total, $200K personal share). This grant is in collaboration with Prof. Toby Li (Notre Dame) and Prof. Yaxing Yao (Virginia Tech).
Feb 1, 2024	Our Special Integret Group proposal titled “Human-Centered Privacy Research in the Age of Large Language Models” is accepted at CHI’24! Excited to meet people from diverse backgrounds at CHI!
Jan 19, 2024	Two papers accepted at CHI’24! One on how LLMs may invade users’ privacy, and another on how LLMs may empower people to build better multimodal apps!
Jan 12, 2024	Our paper on the Matcha IDE plugin is accepted at IMWUT! Try out the tool.
May 3, 2023	Job search update: I’ll be joining the Khoury College of Computer Sciences at Northeastern University as an Assistant Professor in Fall 2024! Before that, I’ll spend a year in the Bay Area, working at Google Checks on privacy compliance intelligence and at Berkeley as a postdoc. Looking forward to the journey ahead!
Apr 20, 2023	Our paper on the COVID-19 contact-tracing app adoption problem won a Best Research Papers 2019-2021 Award at Pervasive and Mobile Computing!

Selected Publications

CSCW

Secret Use of Large Language Models

Zhiping Zhang, Chenxinran Shen, Bingsheng Yao, Dakuo Wang, and Tianshi Li

In CSCW 2025 Oct 2025

Abs arXiv

The advancements of Large Language Models (LLMs) have decentralized the responsibility for the transparency of AI usage. Specifically, LLM users are now encouraged or required to disclose the use of LLM-generated content for varied types of real-world tasks. However, an emerging phenomenon, users’ secret use of LLMs, raises challenges in ensuring end users adhere to the transparency requirement. Our study used mixed-methods with an exploratory survey (125 real-world secret use cases reported) and a controlled experiment among 300 users to investigate the contexts and causes behind the secret use of LLMs. We found that such secretive behavior is often triggered by certain tasks, transcending demographic and personality differences among users. Task types were found to affect users’ intentions to use secretive behavior, primarily through influencing of perceived external judgment regarding LLM usage. Our results yield important insights for future work on designing interventions to encourage more transparent disclosure of LLM/AI use
NeurIPS

PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action

Yijia Shao, Tianshi Li, Weiyan Shi, Yanchen Liu, and Diyi Yang

In NeurIPS Datasets and Benchmarks Sep 2024

arXiv Code Website
CHI SIG

Human-Centered Privacy Research in the Age of Large Language Models

Tianshi Li, Sauvik Das, Hao-Ping (Hank) Lee, Dakuo Wang, Bingsheng Yao, and Zhiping Zhang

In CHI Conference on Human Factors in Computing Systems (CHI’24 Companion) Apr 2024

Abs arXiv

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 has been model-centered: exploring how LLMs lead to privacy risks like memorization, or can be used to infer personal characteristics about people from their content. We argue that there is a need for more research focusing on the human aspect of these privacy issues: e.g., research on how design paradigms for LLMs affect users’ disclosure behaviors, users’ mental models and preferences for privacy controls, and the design of tools, systems, and artifacts that empower end-users to reclaim ownership over their personal data. To build usable, efficient, and privacy-friendly systems powered by these models with imperfect privacy properties, our goal is to initiate discussions to outline an agenda for conducting human-centered research on privacy issues in LLM-powered systems. This Special Interest Group (SIG) aims to bring together researchers with backgrounds in usable security and privacy, human-AI collaboration, NLP, or any other related domains to share their perspectives and experiences on this problem, to help our community establish a collective understanding of the challenges, research opportunities, research methods, and strategies to collaborate with researchers outside of HCI.
CHI

“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 (Hank) Lee, Bingsheng Yao, Sauvik Das, Ada Lerner, Dakuo Wang, and Tianshi Li

In CHI Conference on Human Factors in Computing Systems Apr 2024

Abs arXiv PDF Talk Website

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 that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users’ perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs. However, users’ erroneous mental models and the dark patterns in system design limited their awareness and comprehension of the privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, which complicated users’ ability to navigate the trade-offs. We discuss practical design guidelines and the needs for paradigmatic shifts to protect the privacy of LLM-based CA users.
IMWUT

Matcha: An IDE Plugin for Creating Accurate Privacy Nutrition Labels

Tianshi Li, Lorrie Faith Cranor, Yuvraj Agarwal, and Jason I Hong

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Apr 2024

Abs arXiv HTML Website

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-reported by developers and have been found to contain many inaccuracies due to misunderstandings of the label taxonomy. In this work, we present Matcha, an IDE plugin that uses automated code analysis to help developers create accurate Google Play data safety labels. Developers can benefit from Matcha’s ability to detect user data accesses and transmissions while staying in control of the generated label by adding custom Java annotations and modifying an auto-generated XML specification. Our evaluation with 12 developers showed that Matcha helped our participants improved the accuracy of a label they created with Google’s official tool for a real-world app they developed. We found that participants preferred Matcha for its accuracy benefits. Drawing on Matcha, we discuss general design recommendations for developer tools used to create accurate standardized privacy notices.
CHI

Understanding Challenges for Developers to Create Accurate Privacy Nutrition Labels

Tianshi Li, Kayla Reiman, Yuvraj Agarwal, Lorrie Faith Cranor, and Jason I Hong

In CHI Conference on Human Factors in Computing Systems Apr 2022

Best Paper Honorable Mention Award 🏅

Abs HTML PDF

Apple announced the introduction of app privacy details to their App Store in December 2020, marking the first ever real-world, large-scale deployment of the privacy nutrition label concept, which had been introduced by researchers over a decade earlier. The Apple labels are created by app developers, who self-report their app’s data practices. In this paper, we present the first study examining the usability and understandability of Apple’s privacy nutrition label creation process from the developer’s perspective. By observing and interviewing 12 iOS app developers about how they created the privacy label for a real-world app that they developed, we identified common challenges for correctly and efficiently creating privacy labels. We discuss design implications both for improving Apple’s privacy label design and for future deployment of other standardized privacy notices.
IMWUT

Honeysuckle: Annotation-Guided Code Generation of In-App Privacy Notices

Tianshi Li, Elijah B Neundorfer, Yuvraj Agarwal, and Jason I Hong

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Sep 2021

Abs HTML PDF Talk Demo

In-app privacy notices can help smartphone users make informed privacy decisions. However, they are rarely used in real-world apps, since developers often lack the knowledge, time, and resources to design and implement them well. We present Honeysuckle, a programming tool that helps Android developers build in-app privacy notices using an annotation-based code generation approach facilitated by an IDE plugin, a build system plugin, and a library. We conducted a within-subjects study with 12 Android developers to evaluate Honeysuckle. Each participant was asked to implement privacy notices for two popular open-source apps using the Honeysuckle library as a baseline as well as the annotation-based approach. Our results show that the annotation-based approach helps developers accomplish the task faster with significantly lower cognitive load. Developers preferred the annotation-based approach over the library approach because it was much easier to learn and use and allowed developers to achieve various types of privacy notices using a unified code format, which can enhance code readability and benefit team collaboration.
CSCW

How Developers Talk About Personal Data and What It Means for User Privacy: A Case Study of a Developer Forum on Reddit

Tianshi Li, Elizabeth Louie, Laura Dabbish, and Jason I Hong

Proc. ACM Hum. Comput. Interact. Jan 2021

Abs HTML PDF Talk

While online developer forums are major resources of knowledge for application developers, their roles in promoting better privacy practices remain underexplored. In this paper, we conducted a qualitative analysis of a sample of 207 threads (4772 unique posts) mentioning different forms of personal data from the /r/androiddev forum on Reddit. We started with bottom-up open coding on the sampled posts to develop a typology of discussions about personal data use and conducted follow-up analyses to understand what types of posts elicited in-depth discussions on privacy issues or mentioned risky data practices. Our results show that Android developers rarely discussed privacy concerns when talking about a specific app design or implementation problem, but often had active discussions around privacy when stimulated by certain external events representing new privacy-enhancing restrictions from the Android operating system, app store policies, or privacy laws. Developers often felt these restrictions could cause considerable cost yet fail to generate any compelling benefit for themselves. Given these results, we present a set of suggestions for Android OS and the app store to design more effective methods to enhance privacy, and for developer forums(e.g., /r/androiddev) to encourage more in-depth privacy discussions and nudge developers to think more about privacy.
PMC

What makes people install a COVID-19 contact-tracing app? Understanding the influence of app design and individual difference on contact-tracing app adoption intention

Tianshi Li, Camille Cobb, Jackie (junrui) Yang, Sagar Baviskar, Yuvraj Agarwal, Beibei Li, Lujo Bauer, and Jason I Hong

Pervasive Mob. Comput. Aug 2021

Best Research Paper 2019-2021 Award 🏆

Abs HTML PDF

Smartphone-based contact-tracing apps are a promising solution to help scale up the conventional contact-tracing process. However, low adoption rates have become a major issue that prevents these apps from achieving their full potential. In this paper, we present a national-scale survey experiment (N=1963) in the U.S. to investigate the effects of app design choices and individual differences on COVID-19 contact-tracing app adoption intentions. We found that individual differences such as prosocialness, COVID-19 risk perceptions, general privacy concerns, technology readiness, and demographic factors played a more important role than app design choices such as decentralized design vs. centralized design, location use, app providers, and the presentation of security risks. Certain app designs could exacerbate the different preferences in different sub-populations which may lead to an inequality of acceptance to certain app design choices (e.g., developed by state health authorities vs. a large tech company) among different groups of people (e.g., people living in rural areas vs. people living in urban areas). Our mediation analysis showed that one’s perception of the public health benefits offered by the app and the adoption willingness of other people had a larger effect in explaining the observed effects of app design choices and individual differences than one’s perception of the app’s security and privacy risks. With these findings, we discuss practical implications on the design, marketing, and deployment of COVID-19 contact-tracing apps in the U.S.
IMWUT

Coconut: An IDE Plugin for Developing Privacy-Friendly Apps

Tianshi Li, Yuvraj Agarwal, and Jason I Hong

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Dec 2018

Abs HTML PDF Demo Website

Although app developers are responsible for protecting users’ privacy, this task can be very challenging. In this paper, we present Coconut, an Android Studio plugin that helps developers handle privacy requirements by engaging developers to think about privacy during the development process and providing real-time feedback on potential privacy issues. We start by presenting new findings based on a series of semi-structured interviews with Android developers, probing into the difficulties with privacy that developers face when building apps. Based on these findings, we implemented a proof-of-concept prototype of Coconut and evaluated it in a controlled lab study with 18 Android developers (including eight professional developers). Our study results suggest that apps developed with Coconut handled privacy concerns better, and the developers that used Coconut had a better understanding of their code’s behavior and wrote a better privacy policy for their app. We also found that requiring developers to do a small amount of annotating work regarding their apps’ personal data practices during the development process may result in a significant improvement in app privacy.