A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …
However, new privacy concerns have also emerged during the aggregation of the …
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 …
Membership inference attacks against machine learning models
We quantitatively investigate how machine learning models leak information about the
individual data records on which they were trained. We focus on the basic membership …
individual data records on which they were trained. We focus on the basic membership …
Stealing machine learning models via prediction {APIs}
Machine learning (ML) models may be deemed confidential due to their sensitive training
data, commercial value, or use in security applications. Increasingly often, confidential ML …
data, commercial value, or use in security applications. Increasingly often, confidential ML …
Privacy-preserving deep learning
R Shokri, V Shmatikov - Proceedings of the 22nd ACM SIGSAC …, 2015 - dl.acm.org
Deep learning based on artificial neural networks is a very popular approach to modeling,
classifying, and recognizing complex data such as images, speech, and text. The …
classifying, and recognizing complex data such as images, speech, and text. The …
How to dp-fy ml: A practical guide to machine learning with differential privacy
Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …
constant focus of research. Modern ML models have become more complex, deeper, and …
Deep models under the GAN: information leakage from collaborative deep learning
Deep Learning has recently become hugely popular in machine learning for its ability to
solve end-to-end learning systems, in which the features and the classifiers are learned …
solve end-to-end learning systems, in which the features and the classifiers are learned …
Privacy risk in machine learning: Analyzing the connection to overfitting
S Yeom, I Giacomelli, M Fredrikson… - 2018 IEEE 31st …, 2018 - ieeexplore.ieee.org
Machine learning algorithms, when applied to sensitive data, pose a distinct threat to
privacy. A growing body of prior work demonstrates that models produced by these …
privacy. A growing body of prior work demonstrates that models produced by these …
Differential privacy for deep and federated learning: A survey
A El Ouadrhiri, A Abdelhadi - IEEE access, 2022 - ieeexplore.ieee.org
Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …
of users may be disclosed during data collection, during training, or even after releasing the …
Wireless network intelligence at the edge
Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …
based machine learning (ML) have transformed every aspect of our lives from face …