Search Results for author:
Found 7 papers, 0 papers with code
Adaptive Domain Inference Attack
As deep neural networks are increasingly deployed in sensitive application domains, such as healthcare and security, it's necessary to understand what kind of sensitive information can be inferred from these models.
A Comparative Study of Image Disguising Methods for Confidential Outsourced Learning
In this paper, we study and compare novel \emph{image disguising} mechanisms, DisguisedNets and InstaHide, aiming to achieve a better trade-off among the level of protection for outsourced DNN model training, the expenses, and the utility of data.
GAN-based Domain Inference Attack
Our basic idea is to use the target model to affect a GAN training process for a candidate domain's dataset that is easy to obtain.
Confidential Machine Learning on Untrusted Platforms: A Survey
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e. g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning.
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning
We develop an image disguising approach to address these attacks and design a suite of methods to evaluate the levels of attack resilience for a privacy-preserving solution for outsourced deep learning.
Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data
We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data.
A General Boosting Method and its Application to Learning Ranking Functions for Web Search
We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems.
