Humboldt-Universität zu Berlin
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Faculty of Mathematics and Natural Sciences
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Department of Computer Science
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Members (engl)
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Philipp Schoppmann
Phillipp Schoppmann

I am a research scientist at Google. I finished my PhD in 2021 as a member of the Computer Engineering Group at Humboldt-University of Berlin. Before, I obtained an MSc in Informatics from the University of Edinburgh in Scotland.
Research
My main areas of interest are
- Information- and Network Security
- Privacy-Enhancing Technologies
- Cryptography
- Distributed Systems
- Secure Multiparty Computation
Contact
schoppmann@informatik.hu-berlin.de
+49 30 2093 3039
D172 1A11 E09B 4281 B155 F672 2250 016F 53AC 6F7E
Post Address
Humboldt-Universität zu Berlin
Institut für Informatik
Unter den Linden 6
10099 Berlin
Visitor Address
Johann-von-Neumann-Haus
Haus IV, Raum 304
Rudower Chaussee 25
12489 Berlin
Publications
Journal Articles
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
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
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
Privacy Preserving Distributed Linear Regression on High-Dimensional Data
Proceedings on Privacy Enhancing Technologies (PETS '17), 2017
URL, DOI, BibTex
Conference Papers
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
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
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
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
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
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
Theses
Workshop Presentations
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
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