Measuring Ad-Blocker Efficiency and Privacy

Course Project, Poster and Report for CMU-17733 - Privacy, Policy, Law and Technology

Abstract

This research study aims to conduct and provide insights into Ad-Blocker efficiency and privacy. It is no surprise that with the advent of the internet, digital advertisements have become commonplace since most people spend a significant time online. These advertisements, while useful sometimes, tend to be intrusive and invade user privacy. We aim to find the best possible ad blocker in terms of efficiency and privacy protection, by running experiments with the most popular ad blocker options on Fortune 250 websites. We aim to derive a comparative result on the basis of our experiments.

Requirements

• Node.js
• Python
• Haralyzer
• Gecko Drive
• Chromium Driver
• Browser Mob proxy

Contributors

• Swadhin Routray
• Jatan Loya

Citation

Please cite the repo if you use the data or code in this repo.

@misc{adblocktracker,
  author = {Swadhin Routray and Jatan Loya and Zili Zhou and Nicholas Park },
  title = {Measuring Ad-Blocker Efficiency and Privacy},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/swadhinroutray/PPLT-Ad-Blocker-Tracker}},
}