Ehsan UH.

My Reseach

Here are some of the research projects that I've worked on, each with a brief description to introduce you with my research.

Do Technical Level and Trust in Service Providers Inform Consumers' Privacy Control Adoption?

SPSS
R
Python

**The paper based on this project received 'Best Paper Award at 2023 ACM CHI Conference on Human Factors in Computing Systems'

As tech companies increasingly collect user data, understanding how users perceive and respond to privacy controls is crucial. This study examines the trust gap between technical and non-technical users in service providers' data collection. By uncovering these disparities, we aim to inform the development of more effective privacy tools that resonate with diverse user groups.

What Characteristics Lead to users' WTP for premium data privacy controls in IoT?

SPSS
R

As the Internet of Things (IoT) expands, so do concerns about personal data privacy. Our research reveals that users are willing to pay for enhanced privacy protection, especially those with higher technical literacy. This finding underscores the need for IoT devices to prioritize user privacy and offer transparent, customizable privacy settings.

Who's to Blame When a Data Breach Occurs?

SPSS
R

This study explores how users' past security actions impact their blame attribution. Surprisingly, we found that non-compliant users tend to deflect blame onto service providers. These insights can help organizations develop more effective pre- and post-breach communication strategies.

How do Users' Calculate Tradeoff between Privacy and Money at IoT Purchase Time?

SPSS
R
Python

**The paper based on this project is currently under review.

As the 'pay for privacy' model gains momentum, we investigated how users weigh data privacy against monetary benefits. Our study, using an incentive-compatible lottery on Prolific, uncovers the subtle calculations that drive user choices in privacy-focused IoT purchases.

How Do Users' Decide to Update Software?

SPSS

**The paper based on this project is currently under review.

This research explores why some users are more likely to update their software than others. While factors like update type (security vs. performance) and required restarts were not significant, trust in the software provider emerged as a key influence. Interestingly, those who intended to update often experienced higher levels of cognitive dissonance, suggesting that the decision to update can be a complex psychological process

How to Efficiently Detect Anime Faces Leveraging Combined Cascade Classifiers?

OpenCV
Java

**Undergraduate thesis

Anime face detection poses unique challenges due to its stylized nature. We address these challenges by training a cascade of classifiers, each focused on a specific facial feature. Our experimental results demonstrate the effectiveness of this approach, achieving a high detection accuracy.