Publikationen

Personalized PATE: Differential Privacy for Machine Learning with Individual Privacy Guarantees

Mühl, C., & Boenisch, F. (2022). Personalized PATE: Differential Privacy for Machine Learning with Individual Privacy Guarantees. arXiv preprint arXiv:2202.10517.

Splitting the Interpose PUF: A Novel Modeling Attack Strategy

Wisiol, N., Mühl, C., Pirnay, N., Nguyen, P. H., Margraf, M., Seifert, J. P., ... & Rührmair, U. (2020). Splitting the interpose PUF: A novel modeling attack strategy. IACR Transactions on Cryptographic Hardware and Embedded Systems, 97-120.