A Psychometric Scale to Measure Individuals’ Value of Other People’s Privacy (VOPP)

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

Researchers invested enormous efforts to understand and mitigate the concerns of users as technologies collect their private data. However, users often undermine other people’s privacy when, e.g., posting other people’s photos online, granting mobile applications to access contacts, or using technologies that continuously sense the surrounding. Research to understand technology adoption and behaviors related to collecting and sharing data about non-users has been severely lacking. An essential step to progress in this direction is to identify and quantify factors that affect technology’s use. Toward this goal, we propose and validate a psychometric scale to measure how much an individual values other people’s privacy. We theoretically grounded the appropriateness and relevance of the construct and empirically demonstrated the scale’s internal consistency and validity. This scale will advance the field by enabling researchers to predict behaviors, design adaptive privacy-enhancing technologies, and develop interventions to raise awareness and mitigate privacy risks. Read more

Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring

Proceedings on Privacy Enhancing Technologies, 2022

Collecting students’ demographic data by educational technologies may pose privacy risks as stuents can be profiled and targeted. Demographic data are often used in conjunction to behavioral data to build predictive models. In this paper, we show that demographic attributes, such as gender and age, may not be causally relavent for usual prediction tasks, and offer constraint optimization and adversarial censoring-based methods to reduce students’ privacy risks. Read more

The Impact of Viral Posts on Visibility and Behavior of Professionals: A Longitudinal Study of Scientists on Twitter

16th International Conference on Web and Social Media, 2022

On social media, due to complex interactions between users’ attention and recommendation algorithms, the visibility of users’ posts can be unpredictable and vary wildly, sometimes creating unexpected viral events for ordinary users. How do such events affect users’ subsequent behaviors and long-term visibility on the platform? We investigate these questions following a matching-based framework using a dataset comprised of tweeting activities and follower graph changes of 17,157 scientists on Twitter. We identified scientists who experienced unusual virality for the first time in their profile lifespan (viral group) and quantified how viral events influence tweeting behaviors and popularity (as measured through follower statistics). After virality, the viral group increased tweeting frequency, their tweets became more objective and focused on fewer topics, and expressed more positive sentiment relative to their pre-virality tweets. Also, their post-virality tweets were more aligned with their professional expertise and similar to the viral tweet compared to past tweets. Finally, the viral group gained more followers in both the short and long terms compared to a control group. Read more


Indiana University, 2020

My dissertation explores a socio-technical approach to protect our privacy in the context of sharing images online, when we are not in full control over sharing our visual data. It proposes machine learning-based technical solutions to detect and obfuscate sensitive image contents, as well as behavioral interventions to encourage social media users to respect and protect others’ privacy. Read more

Automatically Detecting Bystanders in Photos to Reduce Privacy Risks

IEEE Symposium on Security and Privacy, 2020

People often appear as other (usually unknown) people’s photos as bystanders, and when these photos are shared online, they pose great privacy threats towards them, especially during an era when advances in machine learning enables adversaries to automatically search, identify, and track people utilizing huge image databases available in the cloud. We propose a machine learning model to automatically detect bystanders in an image, so that they can be obfuscated before before posting that photo online. Read more