S. Stammler, T. Kussel, P. Schoppmann, F. Stampe, G. Tremper, S. Katzenbeisser, K. Hamacher, and M. Lablans
Mainzelliste SecureEpiLinker (MainSEL): Privacy-Preserving Record Linkage using Secure Multi-Party Computation
Bioinformatics, 2020
URL, DOI, BibTex

P. Schoppmann, L. Vogelsang, A. Gascón, and B. Balle
Secure and Scalable Document Similarity on Distributed Databases: Differential Privacy to the Rescue
Proceedings on Privacy Enhancing Technologies (PETS '20), 2020
URL, DOI, BibTex

A. Gascón, P. Schoppmann, B. Balle, M. Raykova, J. Doerner, S. Zahur, and D. Evans
Privacy Preserving Distributed Linear Regression on High-Dimensional Data
Proceedings on Privacy Enhancing Technologies (PETS '17), 2017
URL, DOI, BibTex

P. Rindal and P. Schoppmann
VOLE-PSI: Fast OPRF and Circuit-PSI from Vector-OLE
Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT '21), 2021
URL, BibTex

A. Ali, T. Lepoint, S. Patel, M. Raykova, P. Schoppmann, K. Seth, and K. Yeo
Communication-Computation Trade-offs in PIR
USENIX Security Symposium, 2021
URL, BibTex

L. Vogelsang, M. Lehne, P. Schoppmann, F. Prasser, S. Thun, B. Scheuermann, and J. Schepers
A Secure Multi-Party Computation Protocol for Time-To-Event Analyses
Proceedings of MIE 2020: Digital Personalized Health and Medicine, IOS Press, 2020
DOI, BibTex

P. Schoppmann, A. Gascón, L. Reichert, and M. Raykova
Distributed Vector-OLE: Improved Constructions and Implementation
ACM Conference on Computer and Communications Security (CCS '19), ACM, 2019
URL, DOI, BibTex

P. Schoppmann, A. Gascón, M. Raykova, and B. Pinkas
Make Some ROOM for the Zeros: Data Sparsity in Secure Distributed Machine Learning
ACM Conference on Computer and Communications Security (CCS '19), ACM, 2019
URL, DOI, BibTex

P. Schoppmann
Secure Computation Protocols for Privacy-Preserving Machine Learning
2021
URL, DOI, BibTex

N. Angelou, A. Benaissa, B. Cebere, W. Clark, A. J. Hall, M. A. Hoeh, D. Liu, P. Papadopoulos, et al.
Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
NeurIPS 2020 Workshop on Privacy Preserving Machine Learning (PPML '20), 2020
URL, BibTex