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publications
A novel approach for constructing emulator for Microsoft Kinect XBOX 360 Sensor in the. NET platform
International Conference on Intelligent Systems, Modelling and Simulation, 2013
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Cartooning for Enhanced Privacy in Lifelogging and Streaming Video
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop on Computer Vision Challenges and Opportunities for Privacy and Security (CV-COPS ’17), 2017
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Viewer Experience of Obscuring Scene Elements in Photos to Enhance Privacy
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018
In this paper, we study the effectiveness and user acceptance of privacy-enhancing image filters Read more
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Conveying Situational Information to People with Visual Impairments
CHI-2019 Workshop: Addressing the Challenges of Situationally-Induced Impairments and Disabilities in Mobile Interaction, 2019
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Can Privacy Be Satisfying?: On Improving Viewer Satisfaction for Privacy-Enhanced Photos Using Aesthetic Transforms
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019
In this paper, we applied artistic image transforms to improve visual aesthetics of privacy-enhanced photos Read more
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Reducing Privacy Risks in the Context of Sharing Photos Online
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020
This is an extended abstract published in the doctoral consortium, CHI-2020. Read more
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Influencing Photo Sharing Decisions on Social Media: A Case of Paradoxical Findings
IEEE Symposium on Security and Privacy, 2020
In this study, we investigated the effectiveness of two textual interventions designed to prime people to adopt more privacy-protective behavior Read more
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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
A SOCIO-TECHNICAL APPROACH TO PROTECTING PEOPLE’S PRIVACY IN THE CONTEXT OF SHARING IMAGES ON SOCIAL MEDIA
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
Your Photo is so Funny that I don’t Mind Violating Your Privacy by Sharing it: Effects of Individual Humor Styles on Online Photo-sharing Behaviors
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021
In this paper, we study how people’s online photo-sharing behaviors differ as a function of their humor type, i.e., propensity of using humor for self-entertainment or social interactions (Martin et al. 2003). Read more