  The concrete efficiency of secure computation has been the focus of many
recent works. In this work, we present concretely-efficient protocols for
secure $3$-party computation (3PC) over a ring of integers modulo $2^{\ell}$
tolerating one corruption, both with semi-honest and malicious security. Owing
to the fact that computation over ring emulates computation over the real-world
system architectures, secure computation over ring has gained momentum of late.
  Cast in the offline-online paradigm, our constructions present the most
efficient online phase in concrete terms. In the semi-honest setting, our
protocol requires communication of $2$ ring elements per multiplication gate
during the {\it online} phase, attaining a per-party cost of {\em less than one
element}. This is achieved for the first time in the regime of 3PC. In the {\it
malicious} setting, our protocol requires communication of $4$ elements per
multiplication gate during the online phase, beating the state-of-the-art
protocol by $5$ elements. Realized with both the security notions of selective
abort and fairness, the malicious protocol with fairness involves slightly more
communication 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-aided
machine-learning (ML) inference for a range of prediction functions-- linear
regression, linear SVM regression, logistic regression, and linear SVM
classification. Our setting considers a model-owner with trained model
parameters and a client with a query, with the latter willing to learn the
prediction of her query based on the model parameters of the former. The inputs
and computation are outsourced to a set of three non-colluding servers. Our
constructions catering to both semi-honest and the malicious world, invariably
perform better than the existing constructions.
