When machine learning meets privacy in 6G: A survey

Y Sun, J Liu, J Wang, Y Cao… - … Surveys & Tutorials, 2020‏ - ieeexplore.ieee.org
The rapid-develo** Artificial Intelligence (AI) technology, fast-growing network traffic, and
emerging intelligent applications (eg, autonomous driving, virtual reality, etc.) urgently …

Securing machine learning in the cloud: A systematic review of cloud machine learning security

A Qayyum, A Ijaz, M Usama, W Iqbal, J Qadir… - Frontiers in big …, 2020‏ - frontiersin.org
With the advances in machine learning (ML) and deep learning (DL) techniques, and the
potency of cloud computing in offering services efficiently and cost-effectively, Machine …

POSEIDON: Privacy-preserving federated neural network learning

S Sav, A Pyrgelis, JR Troncoso-Pastoriza… - arxiv preprint arxiv …, 2020‏ - arxiv.org
In this paper, we address the problem of privacy-preserving training and evaluation of neural
networks in an $ N $-party, federated learning setting. We propose a novel system …

When homomorphic encryption marries secret sharing: Secure large-scale sparse logistic regression and applications in risk control

C Chen, J Zhou, L Wang, X Wu, W Fang, J Tan… - Proceedings of the 27th …, 2021‏ - dl.acm.org
Logistic Regression (LR) is the most widely used machine learning model in industry for its
efficiency, robustness, and interpretability. Due to the problem of data isolation and the …

Scalable privacy-preserving distributed learning

D Froelicher, JR Troncoso-Pastoriza, A Pyrgelis… - arxiv preprint arxiv …, 2020‏ - arxiv.org
In this paper, we address the problem of privacy-preserving distributed learning and the
evaluation of machine-learning models by analyzing it in the widespread MapReduce …

SecureNLP: A system for multi-party privacy-preserving natural language processing

Q Feng, D He, Z Liu, H Wang… - IEEE Transactions on …, 2020‏ - ieeexplore.ieee.org
Natural language processing (NLP) allows a computer program to understand human
language as it is spoken, and has been increasingly deployed in a growing number of …

A multicenter random forest model for effective prognosis prediction in collaborative clinical research network

J Li, Y Tian, Y Zhu, T Zhou, J Li, K Ding, J Li - Artificial intelligence in …, 2020‏ - Elsevier
Background The accuracy of a prognostic prediction model has become an essential aspect
of the quality and reliability of the health-related decisions made by clinicians in modern …

A comprehensive survey on secure outsourced computation and its applications

Y Yang, X Huang, X Liu, H Cheng, J Weng, X Luo… - IEEE …, 2019‏ - ieeexplore.ieee.org
With the ever-increasing requirement of storage and computation resources, it is unrealistic
for local devices (with limited sources) to implement large-scale data processing. Therefore …

HE-friendly algorithm for privacy-preserving SVM training

S Park, J Byun, J Lee, JH Cheon, J Lee - IEEE Access, 2020‏ - ieeexplore.ieee.org
Support vector machine (SVM) is one of the most popular machine learning algorithms. It
predicts a pre-defined output variable in real-world applications. Machine learning on …

[HTML][HTML] Multi-fault detection and classification of wind turbines using stacking classifier

P Waqas Khan, YC Byun - Sensors, 2022‏ - mdpi.com
Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest
issue with a wind turbine is reducing failures and downtime, which lowers costs associated …