12 citations · 21 across the 6 of their papers we have counts for
6 papers
A Response to: A Note on "Privacy Preserving n-Party Scalar Product Protocol"
Florian van Daalen, Lianne Ippel, Andre Dekker +1
We reply to the comments on our proposed privacy preserving n-party scalar product protocol made by Liu. In their comment Liu raised concerns regarding the security and scalability…
Federated Bayesian Network Ensembles
Florian van Daalen, Lianne Ippel, Andre Dekker +1
Federated learning allows us to run machine learning algorithms on decentralized data when data sharing is not permitted due to privacy concerns. Ensemble-based learning works by t…
VertiBayes: Learning Bayesian network parameters from vertically partitioned data with missing values
Florian van Daalen, Lianne Ippel, Andre Dekker +1
Federated learning makes it possible to train a machine learning model on decentralized data. Bayesian networks are probabilistic graphical models that have been widely used in art…
Privacy preserving n-party scalar product protocol
Florian van Daalen, Inigo Bermejo, Lianne Ippel +1
Privacy-preserving machine learning enables the training of models on decentralized datasets without the need to reveal the data, both on horizontal and vertically partitioned data…
Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent
Erik-Jan van Kesteren, Chang Sun, Daniel L. Oberski +2
Combining data from varied sources has considerable potential for knowledge discovery: collaborating data parties can mine data in an expanded feature space, allowing them to explo…
Analyzing Partitioned FAIR Health Data Responsibly
Chang Sun, Lianne Ippel, Birgit Wouters +11
It is widely anticipated that the use of health-related big data will enable further understanding and improvements in human health and wellbeing. Our current project, funded throu…