@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 “MADESI”. 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) — 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",
}