Tianshi Li

Starting Fall 2024, I will be an Assistant Professor at Northeastern University in the Khoury College of Computer Sciences. That means:

📢 I’ll be recruiting Ph.D. students for Fall 2024!

If you’re interested in designing and building human-centered solutions for privacy, or developer tools for social good, let’s connect! Read my research statement to learn more about my research interests and research philosophy. I also have new projects studying the emerging privacy issues in human-LLM interactions. Read my recent preprint to learn more.

My research interests lie at the intersection of HCI, privacy, and software engineering. I strive to address the increasing privacy issues in software systems by making it easier for developers to recognize, mitigate, and communicate privacy risks in the systems they program. I apply a human-centered lens to study the challenges developers face with regard to privacy, and I design, build, and evaluate IDE plugins to tackle these challenges by breaking down privacy responsibilities into lightweight annotating tasks, making privacy-enhancing considerations and actions part of the normal development process. My work has been recognized with an ACM CHI best paper honorable mention award and supported in part by a CMU CyLab Presidential Fellowship.

News

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!
Feb 20, 2023	Excited to share my first funded grant from CMU CyLab, titled “Engaging End Users and Developers to Improve App Store Privacy Audits”!
Dec 16, 2022	Just passed my dissertation defense. I’m a soon-to-be Ph.D. now!
Oct 12, 2022	Matcha is released on the JetBrains plugin store!
May 1, 2022	Our paper on C-PAK, a novel mobile text entry technique is accepted to TOCHI 2022.
Apr 1, 2022	Our CHI’22 paper “Understanding Challenges for Developers to Create Accurate Privacy Nutrition Labels.” won a best paper honorable mention award.
Mar 1, 2022	Proposed my Ph.D. dissertation “Privacy-Enhancing Development Environment”
Feb 1, 2022	Our paper on developers’ challenges about privacy regulation features in ad networks is accepted to PETS 2022.
Jan 1, 2022	I started serving on the CHI 2022 Late-Breaking Work (LBW) Program Committee.

Selected Publications

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.
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.