  The growing volumes of data being collected and its analysis to provide
better services are creating worries about digital privacy. To address privacy
concerns and give practical solutions, the literature has relied on secure
multiparty computation. However, recent research has mostly focused on the
small-party honest-majority setting of up to four parties, noting efficiency
concerns. In this work, we extend the strategies to support a larger number of
participants in an honest-majority setting with efficiency at the center stage.
  Cast in the preprocessing paradigm, our semi-honest protocol improves the
online complexity of the decade-old state-of-the-art protocol of Damg\aa rd and
Nielson (CRYPTO'07). In addition to having an improved online communication
cost, we can shut down almost half of the parties in the online phase, thereby
saving up to 50% in the system's operational costs. Our maliciously secure
protocol also enjoys similar benefits and requires only half of the parties,
except for one-time verification, towards the end.
  To showcase the practicality of the designed protocols, we benchmark popular
applications such as deep neural networks, graph neural networks, genome
sequence matching, and biometric matching using prototype implementations. Our
improved protocols aid in bringing up to 60-80% savings in monetary cost over
prior work.
