Estimating random-intercept models on data streams. - Lianne Ippel; Maurits Clemens Kaptein; Jeroen K. Vermunt (Comput. Stat. Data Anal., 2016)

Analyzing Partitioned FAIR Health Data Responsibly. - Chang Sun 001; Lianne Ippel; Birgit Wouters; Johan van Soest; Alexander Malic; Onaopepo Adekunle; Bob van den Berg; Marco Puts; Ole Mussmann; Annemarie Koster; Carla van der Kallen; David Townend; et al. (CoRR, 2018)

A Privacy-Preserving Infrastructure for Analyzing Personal Health Data in a Vertically Partitioned Scenario. - Chang Sun 001; Lianne Ippel; Johan van Soest; Birgit Wouters; Alexander Malic; Onaopepo Adekunle; Bob van den Berg; Ole Mussmann; Annemarie Koster; Carla van der Kallen; Claudia van Oppen; David Townend; et al. (MedInfo, 2019)

Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent. - Erik-Jan van Kesteren; Chang Sun 001; Daniel L. Oberski; Michel Dumontier; Lianne Ippel (CoRR, 2019)

Online estimation of individual-level effects using streaming shrinkage factors. - Lianne Ippel; Maurits Clemens Kaptein; Jeroen K. Vermunt (Comput. Stat. Data Anal., 2019)

Privacy preserving n-party scalar product protocol. - Florian Van Daalen; Iñigo Bermejo; Lianne Ippel; Andre Dekker (CoRR, 2021)

A systematic review on privacy-preserving distributed data mining. - Chang Sun 001; Lianne Ippel; Andre Dekker; Michel Dumontier; Johan van Soest (Data Sci., 2021)

VertiBayes: Learning Bayesian network parameters from vertically partitioned data with missing values. - Florian Van Daalen; Lianne Ippel; Andre Dekker; Iñigo Bermejo (CoRR, 2022)

Federated Bayesian Network Ensembles. - Florian Van Daalen; Lianne Ippel; Andre Dekker; Iñigo Bermejo (FMEC, 2023)

Privacy Preserving $n$n-Party Scalar Product Protocol. - Florian Van Daalen; Lianne Ippel; Andre Dekker; Iñigo Bermejo (IEEE Trans. Parallel Distributed Syst., 2023)

A Response to: A Note on "Privacy Preserving n-Party Scalar Product Protocol". - Florian Van Daalen; Lianne Ippel; Andre Dekker; Iñigo Bermejo (CoRR, 2024)