P. Schoppmann. Secure Computation Protocols for Privacy-Preserving Machine Learning. 2021.

A. Ali, T. Lepoint, S. Patel, M. Raykova, P. Schoppmann, K. Seth, and K. Yeo. Communication–Computation Trade-offs in PIR. USENIX Security Symposium, pages 1811-1828, 2021.

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.

N. Angelou, A. Benaissa, B. Cebere, W. Clark, A. J. Hall, M. A. Hoeh, D. Liu, P. Papadopoulos, R. Roehm, R. Sandmann, P. Schoppmann, and T. Titcombe. 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), November 2020.

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), volume 2020, issue 2, pages 209-229, 2020.

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, volume 270, pages 8-12, 2020.

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.

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.

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.

P. Schoppmann, A. Gascón, and B. Balle. Private Nearest Neighbors Classification in Federated Databases. IACR Cryptology ePrint Archive, volume 2018, March 2018.

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, De Gruyter Open, volume 2017, issue 4, October 2017.