1.9k citations · 1.9k across the 16 of their papers we have counts for
16 papers
Evaluating Google's Protected Audience Protocol
Minjun Long, David Evans
While third-party cookies have been a key component of the digital marketing ecosystem for years, they allow users to be tracked across web sites in ways that raise serious privacy…
DP-RuL: Differentially-Private Rule Learning for Clinical Decision Support Systems
Josephine Lamp, Lu Feng, David Evans
Serious privacy concerns arise with the use of patient data in rule-based clinical decision support systems (CDSS). The goal of a privacy-preserving CDSS is to learn a population r…
Understanding Variation in Subpopulation Susceptibility to Poisoning Attacks
Evan Rose, Fnu Suya, David Evans
Machine learning is susceptible to poisoning attacks, in which an attacker controls a small fraction of the training data and chooses that data with the goal of inducing some behav…
What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?
Fnu Suya, Xiao Zhang, Yuan Tian +1
We study indiscriminate poisoning for linear learners where an adversary injects a few crafted examples into the training data with the goal of forcing the induced model to incur h…
Manipulating Transfer Learning for Property Inference
Yulong Tian, Fnu Suya, Anshuman Suri +2
Transfer learning is a popular method for tuning pretrained (upstream) models for different downstream tasks using limited data and computational resources. We study how an adversa…
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans +5
Deploying machine learning models in production may allow adversaries to infer sensitive information about training data. There is a vast literature analyzing different types of in…