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The concrete efficiency of secure computation has been the focus of manyrecent works. In this work, we present concretely-efficient protocols forsecure $3$-party computation (3PC) over a ring of integers modulo $2^{\ell}$tolerating one corruption, both with semi-honest and malicious security. Owingto the fact that computation over ring emulates computation over the real-worldsystem architectures, secure computation over ring has gained momentum of late.  Cast in the offline-online paradigm, our constructions present the mostefficient online phase in concrete terms. In the semi-honest setting, ourprotocol requires communication of $2$ ring elements per multiplication gateduring the {\it online} phase, attaining a per-party cost of {\em less than oneelement}. This is achieved for the first time in the regime of 3PC. In the {\itmalicious} setting, our protocol requires communication of $4$ elements permultiplication gate during the online phase, beating the state-of-the-artprotocol by $5$ elements. Realized with both the security notions of selectiveabort and fairness, the malicious protocol with fairness involves slightly morecommunication than its counterpart with abort security for the output gates{\em alone}.  We apply our techniques from $3$PC in the regime of secure server-aidedmachine-learning (ML) inference for a range of prediction functions-- linearregression, linear SVM regression, logistic regression, and linear SVMclassification. Our setting considers a model-owner with trained modelparameters and a client with a query, with the latter willing to learn theprediction of her query based on the model parameters of the former. The inputsand computation are outsourced to a set of three non-colluding servers. Ourconstructions catering to both semi-honest and the malicious world, invariablyperform better than the existing constructions.

ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction