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 …
networks in an $ N $-party, federated learning setting. We propose a novel system …
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 …
evaluation of machine-learning models by analyzing it in the widespread MapReduce …
Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryption
DNA methylation data play a crucial role in estimating chronological age in mammals,
offering real-time insights into an individual's aging process. The epigenetic pacemaker …
offering real-time insights into an individual's aging process. The epigenetic pacemaker …
Efficient and privacy-preserving arbitrary polygon range query scheme over dynamic and time-series location data
Location-based services (LBSs) provide enhanced functionality of mobile applications and
convenience for mobile users, which plays a more and more remarkable role in people's …
convenience for mobile users, which plays a more and more remarkable role in people's …
Achievable CCA2 relaxation for homomorphic encryption
Homomorphic encryption (HE) protects data in-use, but can be computationally expensive.
To avoid the costly bootstrapping procedure that refreshes ciphertexts, some works have …
To avoid the costly bootstrapping procedure that refreshes ciphertexts, some works have …
Toward practical privacy-preserving linear regression
W Xu, B Wang, J Liu, Y Chen, P Duan, Z Hong - Information Sciences, 2022 - Elsevier
Linear regression is an ordinary machine learning algorithm that models the relation
between the input values and the output ones with underlying linear functions. Giacomelli et …
between the input values and the output ones with underlying linear functions. Giacomelli et …
两方参与的隐私保护岭回归方案与应用
吕由， 吴文渊 - Journal of Cryptologic Research, 2023 - search.proquest.com
大数据环境下, 同态加密可以有效解决机器学习中的隐私泄露问题. 本文利用CKKS
同态加密技术, 设计了一种两方参与, 基于密文域上带除法延迟的改进共轭梯度法的隐私保护岭 …
同态加密技术, 设计了一种两方参与, 基于密文域上带除法延迟的改进共轭梯度法的隐私保护岭 …
Privacy-preserving federated recurrent neural networks
We present RHODE, a novel system that enables privacy-preserving training of and
prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting …
prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting …
A privacy-preserving state estimation scheme for smart grids
With the appearance of electric energy market deregulation, there exists a growing concern
over the potential privacy leakage of commercial data among competing power companies …
over the potential privacy leakage of commercial data among competing power companies …
Efficient privacy-preserving viral strain classification via k-mer signatures and FHE
With the development of sequencing technologies, viral strain classification-which is critical
for many applications, including disease monitoring and control-has become widely …
for many applications, including disease monitoring and control-has become widely …