A survey and guideline on privacy enhancing technologies for collaborative machine learning
As machine learning and artificial intelligence (ML/AI) are becoming more popular and
advanced, there is a wish to turn sensitive data into valuable information via ML/AI …
advanced, there is a wish to turn sensitive data into valuable information via ML/AI …
Efficient data collaboration using multi-party privacy preserving machine learning framework
In a modern era where data-driven insights are the foundation of technological
advancements, preserving the privacy and security of sensitive information while harnessing …
advancements, preserving the privacy and security of sensitive information while harnessing …
Ppfl: Enhancing privacy in federated learning with confidential computing
Mobile networks and devices provide the users with ubiquitous connectivity, while many of
their functionality and business models rely on data analysis and processing. In this context …
their functionality and business models rely on data analysis and processing. In this context …
Exploring homomorphic encryption and differential privacy techniques towards secure federated learning paradigm
The trend of the next generation of the internet has already been scrutinized by top analytics
enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the …
enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the …
Practical private aggregation in federated learning against inference attack
P Zhao, Z Cao, J Jiang, F Gao - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Federated learning (FL) enables multiple worker devices share local models trained on their
private data to collaboratively train a machine learning model. However, local models are …
private data to collaboratively train a machine learning model. However, local models are …
Privcoll: Practical privacy-preserving collaborative machine learning
Collaborative learning enables two or more participants, each with their own training
dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not …
dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not …
A method to improve the privacy and security for federated learning
Y Bai, M Fan - 2021 IEEE 6th international conference on …, 2021 - ieeexplore.ieee.org
Federated learning is proposed as an approach that enables several participants to
collaboratively train the machine learning model, but without directly expose the local data to …
collaboratively train the machine learning model, but without directly expose the local data to …
Falcon: A privacy-preserving and interpretable vertical federated learning system
Federated learning (FL) enables multiple data owners to collaboratively train machine
learning (ML) models without disclosing their raw data. In the vertical federated learning …
learning (ML) models without disclosing their raw data. In the vertical federated learning …
[HTML][HTML] Privacy preservation in federated learning: An insightful survey from the GDPR perspective
In recent years, along with the blooming of Machine Learning (ML)-based applications and
services, ensuring data privacy and security have become a critical obligation. ML-based …
services, ensuring data privacy and security have become a critical obligation. ML-based …
[图书][B] Privacy-preserving Deep Learning: A Comprehensive Survey
K Kim, HC Tanuwidjaja - 2021 - Springer
This monograph aims to give a survey on the state of the art of Privacy-Preserving Deep
Learning (PPDL), which is considered to be one of the emerging technologies by combining …
Learning (PPDL), which is considered to be one of the emerging technologies by combining …