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Papers

We are the Brave Research team! We work at the intersection of theory and practice on many topics in Computer Science. Our research mainly focuses on privacy, security, machine-learning, networks, and measurements. Read our work here!

2022

Venue Title Topic
IMC Measuring UID Smuggling in the Wild Privacy
CCS STAR: Secret Sharing for Private Threshold Aggregation Reporting Cryptography, Privacy
PETS FrodoPIR: Simple, Scalable, Single-Server Private Information Retrieval Cryptography, Privacy
eprint A Framework for Building Secure, Scalable, Networked Enclaves Networks, Privacy
PETS Blocked or Broken? Automatically Detecting When Privacy Interventions Break Websites Privacy
WWW Measuring the Privacy vs. Compatibility Trade-off in Preventing Third-Party Stateful Tracking Privacy

2021

Venue Title Topic
CCS SugarCoat: Programmatically Generating Privacy-Preserving, Web-Compatible Resource Replacements for Content Blocking Privacy
eprint PrivateFetch: Scalable Catalog Delivery in Privacy-Preserving Advertising Cryptography
eprint What's in Your Wallet? Privacy and Security Issues in Web 3.0 Privacy, Security, Measurements
PETS zkSENSE: A Friction-less Privacy-Preserving Human Attestation Mechanism for Mobile Devices Cryptography, Privacy
RecSys Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback Machine Learning, Privacy
IEEE S&P Detecting Filter List Evasion With Event-Loop-Turn Granularity JavaScript Signatures Security
WWW Towards Realistic and Reproducible Web Crawl Measurements Measurements

2020

Venue Title Topic
CoNEXT Mind the Delay: The Adverse Effects of Delay-Based TCP on HTTP Networks
eprint There's No Trick, Its Just a Simple Trick: A Web-Compat and Privacy Improving Approach to Third-Party Web Storage Privacy
USENIX ATC Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning Machine Learning, Privacy
SIGMETRICS Who Filters the Filters: Understanding the Growth, Usefulness and Efficiency of Crowdsourced Ad Blocking Machine Learning, Privacy
IEEE S&P AdGraph: A Machine Learning Approach to Automatic and Effective Adblocking Machine Learning, Privacy
MLSys Privacy-Preserving Bandits Machine Learning, Privacy
WWW Keeping Out the Masses: Understanding the Popularity and Implications of Internet Paywalls Privacy
WWW Filter List Generation for Underserved Regions Privacy
CHI Evaluating the End-User Experience of Private Browsing Mode Machine Learning, Privacy
WWW De-Kodi: Understanding the Kodi Ecosystem Privacy

2019

Venue Title Topic
WWW SpeedReader: Reader Mode Made Fast and Private Privacy
HotNets BatteryLab: A Distributed Platform for Battery Measurements Measurements
eprint The Blind Men and the Internet: Multi-Vantage Point Web Measurements Privacy
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