| Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption S Hardy, W Henecka, H Ivey-Law, R Nock, G Patrini, G Smith, B Thorne arXiv preprint arXiv:1711.10677, 2017 | 685 | 2017 |
| TASTY: tool for automating secure two-party computations W Henecka, S K ögl, AR Sadeghi, T Schneider, I Wehrenberg Proceedings of the 17th ACM conference on Computer and communications …, 2010 | 506 | 2010 |
| Entity resolution and federated learning get a federated resolution R Nock, S Hardy, W Henecka, H Ivey-Law, G Patrini, G Smith, B Thorne arXiv preprint arXiv:1803.04035, 2018 | 117 | 2018 |
| Correcting errors in RSA private keys W Henecka, A May, A Meurer Annual Cryptology Conference, 351-369, 2010 | 74 | 2010 |
| Faster secure two-party computation with less memory W Henecka, T Schneider Proceedings of the 8th ACM SIGSAC symposium on Information, computer and …, 2013 | 56 | 2013 |
| Strip: Privacy-preserving vector-based routing W Henecka, M Roughan 2013 21st IEEE International Conference on Network Protocols (ICNP), 1-10, 2013 | 24 | 2013 |
| Automatic generation of sigma-protocols E Bangerter, T Briner, W Henecka, S Krenn, AR Sadeghi, T Schneider European Public Key Infrastructure Workshop, 67-82, 2009 | 23 | 2009 |
| Privacy-preserving fraud detection across multiple phone record databases W Henecka, M Roughan IEEE Transactions on Dependable and Secure Computing 12 (6), 640-651, 2014 | 22 | 2014 |
| Lossy compression of dynamic, weighted graphs W Henecka, M Roughan 2015 3rd International Conference on Future Internet of Things and Cloud …, 2015 | 13 | 2015 |
| The impact of record linkage on learning from feature partitioned data R Nock, S Hardy, W Henecka, H Ivey-Law, J Nabaglo, G Patrini, G Smith, ... International Conference on Machine Learning, 8216-8226, 2021 | 12 | 2021 |
| Privacy-preserving entity resolution and logistic regression on encrypted data M Djatmiko, S Hardy, W Henecka, H Ivey-Law, M Ott, G Patrini, G Smith, ... Private and Secure Machine Learning (PSML), 2017 | 8 | 2017 |
| Fairness-aware privacy-preserving record linkage D Vatsalan, J Yu, W Henecka, B Thorne Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS …, 2020 | 5 | 2020 |
| Hyper-parameter optimization for privacy-preserving record linkage J Yu, J Nabaglo, D Vatsalan, W Henecka, B Thorne ECML PKDD 2020 Workshops: Workshops of the European Conference on Machine …, 2020 | 3 | 2020 |
| A measure of personal information in mobile data I Oppermann, J Nabaglo, W Henecka 2020 2nd 6G Wireless Summit (6G SUMMIT), 1-6, 2020 | 2 | 2020 |
| Boosted and differentially private ensembles of decision trees R Nock, W Henecka arXiv preprint arXiv:2001.09384, 2020 | 2 | 2020 |
| P-signature-based blocking to improve the scalability of privacy-preserving record linkage D Vatsalan, J Yu, B Thorne, W Henecka Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS …, 2020 | 2 | 2020 |
| Network management in a world of secrets. W Henecka | | 2015 |
| Conversion of real-numbered privacy-preserving problems into the integer domain W Henecka, N Bean, M Roughan Information and Communications Security: 14th International Conference …, 2012 | | 2012 |
| The Impact of Record Linkage on Learning from Feature Partitioned Data—Supplementary Material— R Nock, S Hardy, W Henecka, H Ivey-Law, J Nabaglo, G Patrini, G Smith, ... | | |
| On Private Supervised Distributed Learning: Weakly Labeled and without Entity Resolution S Hardy, W Henecka, R Nock | | |
