Preprint
Navigating Cookie Compliance Around the Globe
(Submission) 34th USENIX Security Symposium (2025)
Brian Tang, Duc Bui, Kang G. Shin
A research project analyzing inconsistencies in cookie consent mechanisms on websites across the globe. We developed ConsentChk, an automated system that detects and categorizes violations between a website’s cookie usage and users’ consent preferences. I contributed to the design, writing, and analysis of cookie consent discrepancies across 1,458 globally-popular websites. Our findings revealed that regional privacy laws and consent management platforms significantly impact cookie consent behavior and violation rates.
Navigating Cookie Compliance Around the Globe
(Submission) 34th USENIX Security Symposium (2025)
A research project analyzing inconsistencies in cookie consent mechanisms on websites across the globe. We developed ConsentChk, an automated system that detects and categorizes violations between a website’s cookie usage and users’ consent preferences. I contributed to the design, writing, and analysis of cookie consent discrepancies across 1,458 globally-popular websites. Our findings revealed that regional privacy laws and consent management platforms significantly impact cookie consent behavior and violation rates.
Rearchitecting Classification Frameworks For Increased Robustness
ArXiv Preprint
Varun Chandrasekaran, Brian Tang, Nicolas Papernot, Kassem Fawaz, Somesh Jha, Xi Wu
A case study and evaluation on how deep neural networks (DNNs) are highly effective but vulnerable to adversarial inputs -- subtle manipulations designed to deceive them. Existing defenses often sacrifice accuracy and require extensive training. Collaborating with the lead author, I implemented a design of a hierarchical classification approach that leverages invariant features to enhance adversarial robustness without compromising accuracy.
Rearchitecting Classification Frameworks For Increased Robustness
ArXiv Preprint
A case study and evaluation on how deep neural networks (DNNs) are highly effective but vulnerable to adversarial inputs -- subtle manipulations designed to deceive them. Existing defenses often sacrifice accuracy and require extensive training. Collaborating with the lead author, I implemented a design of a hierarchical classification approach that leverages invariant features to enhance adversarial robustness without compromising accuracy.
