@inproceedings{d7f2063370a943b59ed9cd098889481b,
title = "CryptoSPN: Expanding PPML beyond Neural Networks",
keywords = "privacy-preserving inference, secure computation, sum-product networks",
author = "Amos Treiber and Alejandro Molina and Christian Weinert and Thomas Schneider and Kristian Kersting",
note = "Funding Information: KK acknowledges the support of the Federal Ministry of Education and Research (BMBF), grant number 01IS18043B {\textquotedblleft}MADESI{\textquotedblright}. This project has received funding from the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation program (grant agreement No. 850990 PSOTI). It was co-funded by the Deutsche Forschungsgemeinschaft (DFG) {\textemdash} SFB 1119 CROSSING/236615297 and GRK 2050 Privacy \& Trust/251805230, and by the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within ATHENE. Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 Workshop on Privacy-Preserving Machine Learning in Practice, PPMLP 2020 ; Conference date: 09-11-2020",
year = "2020",
month = nov,
day = "9",
doi = "10.1145/3411501.3419417",
language = "English",
series = "PPMLP 2020 - Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice",
publisher = "Association for Computing Machinery, Inc",
pages = "9--14",
booktitle = "PPMLP 2020 - Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice",
}