18 citations · 75 across the 11 of their papers we have counts for
11 papers
DHBE: Data-free Holistic Backdoor Erasing in Deep Neural Networks via Restricted Adversarial Distillation
Zhicong Yan, Shenghong Li, Ruijie Zhao +2
Backdoor attacks have emerged as an urgent threat to Deep Neural Networks (DNNs), where victim DNNs are furtively implanted with malicious neurons that could be triggered by the ad…
Towards Return Parity in Markov Decision Processes
Jianfeng Chi, Jian Shen, Xinyi Dai +3
Algorithmic decisions made by machine learning models in high-stakes domains may have lasting impacts over time. However, naive applications of standard fairness criterion in stati…
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
Sijun Tan, Brian Knott, Yuan Tian +1
We introduce CryptGPU, a system for privacy-preserving machine learning that implements all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in…
Meta Federated Learning
Omid Aramoon, Pin-Yu Chen, Gang Qu +1
Due to its distributed methodology alongside its privacy-preserving features, Federated Learning (FL) is vulnerable to training time adversarial attacks. In this study, our focus i…
Understanding and Mitigating Accuracy Disparity in Regression
Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon +1
With the widespread deployment of large-scale prediction systems in high-stakes domains, e.g., face recognition, criminal justice, etc., disparity in prediction accuracy between di…
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Syed Zawad, Ahsan Ali, Pin-Yu Chen +5
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on…
PolicyQA: A Reading Comprehension Dataset for Privacy Policies
Wasi Uddin Ahmad, Jianfeng Chi, Yuan Tian +1
Privacy policy documents are long and verbose. A question answering (QA) system can assist users in finding the information that is relevant and important to them. Prior studies in…
Model-Targeted Poisoning Attacks with Provable Convergence
Fnu Suya, Saeed Mahloujifar, Anshuman Suri +2
In a poisoning attack, an adversary with control over a small fraction of the training data attempts to select that data in a way that induces a corrupted model that misbehaves in…
Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries
Fnu Suya, Jianfeng Chi, David Evans +1
We study adversarial examples in a black-box setting where the adversary only has API access to the target model and each query is expensive. Prior work on black-box adversarial ex…
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation
Han Zhao, Jianfeng Chi, Yuan Tian +1
Crowdsourced data used in machine learning services might carry sensitive information about attributes that users do not want to share. Various methods have been proposed to minimi…
Query-limited Black-box Attacks to Classifiers
Fnu Suya, Yuan Tian, David Evans +1
We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing t…