61 citations · 177 across the 20 of their papers we have counts for
20 papers
"Private Prediction Strikes Back!'' Private Kernelized Nearest Neighbors with Individual Renyi Filter
Yuqing Zhu, Xuandong Zhao, Chuan Guo +1
Most existing approaches of differentially private (DP) machine learning focus on private training. Despite its many advantages, private training lacks the flexibility in adapting…
Information Flow Control in Machine Learning through Modular Model Architecture
Trishita Tiwari, Suchin Gururangan, Chuan Guo +7
In today's machine learning (ML) models, any part of the training data can affect the model output. This lack of control for information flow from training data to model output is…
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
Kiwan Maeng, Chuan Guo, Sanjay Kariyappa +1
Privacy-preserving instance encoding aims to encode raw data as feature vectors without revealing their privacy-sensitive information. When designed properly, these encodings can b…
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
Chuan Guo, Kamalika Chaudhuri, Pierre Stock +1
In private federated learning (FL), a server aggregates differentially private updates from a large number of clients in order to train a machine learning model. The main challenge…
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi
Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning. However, exactly how small the privacy parameter needs…
Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information
Kiwan Maeng, Chuan Guo, Sanjay Kariyappa +1
Split learning and inference propose to run training/inference of a large model that is split across client devices and the cloud. However, such a model splitting imposes privacy c…
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis
Sanjay Kariyappa, Chuan Guo, Kiwan Maeng +4
Federated learning (FL) aims to perform privacy-preserving machine learning on distributed data held by multiple data owners. To this end, FL requires the data owners to perform tr…
Privacy-Aware Compression for Federated Data Analysis
Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
Federated data analytics is a framework for distributed data analysis where a server compiles noisy responses from a group of distributed low-bandwidth user devices to estimate agg…
Submix: Practical Private Prediction for Large-Scale Language Models
Antonio Ginart, Laurens van der Maaten, James Zou +1
Recent data-extraction attacks have exposed that language models can memorize some training samples verbatim. This is a vulnerability that can compromise the privacy of the model's…
Bounding Training Data Reconstruction in Private (Deep) Learning
Chuan Guo, Brian Karrer, Kamalika Chaudhuri +1
Differential privacy is widely accepted as the de facto method for preventing data leakage in ML, and conventional wisdom suggests that it offers strong protection against privacy…
EIFFeL: Ensuring Integrity for Federated Learning
Amrita Roy Chowdhury, Chuan Guo, Somesh Jha +1
Federated learning (FL) enables clients to collaborate with a server to train a machine learning model. To ensure privacy, the server performs secure aggregation of updates from th…
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Weizhe Hua, Yichi Zhang, Chuan Guo +2
Neural network robustness has become a central topic in machine learning in recent years. Most training algorithms that improve the model's robustness to adversarial and common cor…
Online Adaptation to Label Distribution Shift
Ruihan Wu, Chuan Guo, Yi Su +1
Machine learning models often encounter distribution shifts when deployed in the real world. In this paper, we focus on adaptation to label distribution shift in the online setting…
Byzantine-Robust and Privacy-Preserving Framework for FedML
Hanieh Hashemi, Yongqin Wang, Chuan Guo +1
Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others…
Making Paper Reviewing Robust to Bid Manipulation Attacks
Ruihan Wu, Chuan Guo, Felix Wu +3
Most computer science conferences rely on paper bidding to assign reviewers to papers. Although paper bidding enables high-quality assignments in days of unprecedented submission n…
Measuring Data Leakage in Machine-Learning Models with Fisher Information
Awni Hannun, Chuan Guo, Laurens van der Maaten
Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Co…
On Hiding Neural Networks Inside Neural Networks
Chuan Guo, Ruihan Wu, Kilian Q. Weinberger
Modern neural networks often contain significantly more parameters than the size of their training data. We show that this excess capacity provides an opportunity for embedding sec…
Secure multiparty computations in floating-point arithmetic
Chuan Guo, Awni Hannun, Brian Knott +3
Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data whi…
Certified Data Removal from Machine Learning Models
Chuan Guo, Tom Goldstein, Awni Hannun +1
Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores inf…
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Tao Yu, Shengyuan Hu, Chuan Guo +2
Natural images are virtually surrounded by low-density misclassified regions that can be efficiently discovered by gradient-guided search --- enabling the generation of adversarial…