TY - GEN
T1 - New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning
AU - Damgård, Ivan Bjerre
AU - Escudero Ospina, Daniel Esteban
AU - Frederiksen, Tore Kasper
AU - Keller, Marcel
AU - Scholl, Peter
AU - Volgushev, Nikolaj
PY - 2019
Y1 - 2019
N2 - At CRYPTO 2018 Cramer et al. presented SPDZ2k , a new secret-sharing based protocol for actively secure multi-party computation against a dishonest majority, that works over rings instead of fields. Their protocol uses slightly more communication than competitive schemes working over fields. However, implementation-wise, their approach allows for arithmetic to be carried out using native 32 or 64-bit CPU operations rather than modulo a large prime. The authors thus conjectured that the increased communication would be more than made up for by the increased efficiency of implementations. In this work we answer their conjecture in the affirmative. We do so by implementing their scheme, and designing and implementing new efficient protocols for equality test, comparison, and truncation over rings. We further show that these operations find application in the machine learning domain, and indeed significantly outperform their field-based competitors. In particular, we implement and benchmark oblivious algorithms for decision tree and support vector machine (SVM) evaluation.
AB - At CRYPTO 2018 Cramer et al. presented SPDZ2k , a new secret-sharing based protocol for actively secure multi-party computation against a dishonest majority, that works over rings instead of fields. Their protocol uses slightly more communication than competitive schemes working over fields. However, implementation-wise, their approach allows for arithmetic to be carried out using native 32 or 64-bit CPU operations rather than modulo a large prime. The authors thus conjectured that the increased communication would be more than made up for by the increased efficiency of implementations. In this work we answer their conjecture in the affirmative. We do so by implementing their scheme, and designing and implementing new efficient protocols for equality test, comparison, and truncation over rings. We further show that these operations find application in the machine learning domain, and indeed significantly outperform their field-based competitors. In particular, we implement and benchmark oblivious algorithms for decision tree and support vector machine (SVM) evaluation.
KW - Decision-trees
KW - MPC
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85072914859&partnerID=8YFLogxK
U2 - 10.1109/SP.2019.00078
DO - 10.1109/SP.2019.00078
M3 - Article in proceedings
SP - 1102
EP - 1120
BT - Proceedings - 2019 IEEE Symposium on Security and Privacy, SP 2019
PB - IEEE
ER -