When machine learning meets privacy: A survey and outlook
The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …
From distributed machine learning to federated learning: A survey
In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …
users, various regions or organizations. Because of laws or regulations, the distributed data …
Deep learning with label differential privacy
Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …
robustness in survey aggregation, and has been widely adopted in applications with …
Robust anomaly detection and backdoor attack detection via differential privacy
Outlier detection and novelty detection are two important topics for anomaly detection.
Suppose the majority of a dataset are drawn from a certain distribution, outlier detection and …
Suppose the majority of a dataset are drawn from a certain distribution, outlier detection and …
Large scale private learning via low-rank reparametrization
We propose a reparametrization scheme to address the challenges of applying differentially
private SGD on large neural networks, which are 1) the huge memory cost of storing …
private SGD on large neural networks, which are 1) the huge memory cost of storing …
Muter: Machine unlearning on adversarially trained models
Abstract Machine unlearning is an emerging task of removing the influence of selected
training datapoints from a trained model upon data deletion requests, which echoes the …
training datapoints from a trained model upon data deletion requests, which echoes the …
Do not let privacy overbill utility: Gradient embedding perturbation for private learning
The privacy leakage of the model about the training data can be bounded in the differential
privacy mechanism. However, for meaningful privacy parameters, a differentially private …
privacy mechanism. However, for meaningful privacy parameters, a differentially private …
Adaptive differential privacy in vertical federated learning for mobility forecasting
FZ Errounda, Y Liu - Future Generation Computer Systems, 2023 - Elsevier
Differential privacy is the de-facto technique for protecting the individuals in the training
dataset and the learning models in deep learning. However, the technique presents two …
dataset and the learning models in deep learning. However, the technique presents two …
Privacy-preserving collaborative learning with automatic transformation search
Collaborative learning has gained great popularity due to its benefit of data privacy
protection: participants can jointly train a Deep Learning model without sharing their training …
protection: participants can jointly train a Deep Learning model without sharing their training …
Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
Cloud-based machine learning inference is an emerging paradigm where users query by
sending their data through a service provider who runs an ML model on that data and …
sending their data through a service provider who runs an ML model on that data and …