61 citations · 164 across the 9 of their papers we have counts for
9 papers
Robust, privacy-preserving, transparent, and auditable on-device blocklisting
Kurt Thomas, Sarah Meiklejohn, Michael A. Specter +4
With the accelerated adoption of end-to-end encryption, there is an opportunity to re-architect security and anti-abuse primitives in a manner that preserves new privacy expectatio…
Poisoning Web-Scale Training Datasets is Practical
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo +6
Deep learning models are often trained on distributed, web-scale datasets crawled from the internet. In this paper, we introduce two new dataset poisoning attacks that intentionall…
"There's so much responsibility on users right now:" Expert Advice for Staying Safer From Hate and Harassment
Miranda Wei, Sunny Consolvo, Patrick Gage Kelley +3
Online hate and harassment poses a threat to the digital safety of people globally. In light of this risk, there is a need to equip as many people as possible with advice to stay s…
Understanding Longitudinal Behaviors of Toxic Accounts on Reddit
Deepak Kumar, Jeff Hancock, Kurt Thomas +1
Toxic comments are the top form of hate and harassment experienced online. While many studies have investigated the types of toxic comments posted online, the effects that such con…
SoK: A Framework for Unifying At-Risk User Research
Noel Warford, Tara Matthews, Kaitlyn Yang +7
At-risk users are people who experience elevated digital security, privacy, and safety threats because of what they do, who they are, where they are, or who they are with. In this…
Designing Toxic Content Classification for a Diversity of Perspectives
Deepak Kumar, Patrick Gage Kelley, Sunny Consolvo +5
In this work, we demonstrate how existing classifiers for identifying toxic comments online fail to generalize to the diverse concerns of Internet users. We survey 17,280 participa…