  In the modern era of computing, machine learning tools have demonstrated
their potential in vital sectors, such as healthcare and finance, to derive
proper inferences. The sensitive and confidential nature of the data in such
sectors raises genuine concerns for data privacy. This motivated the area of
Privacy-preserving Machine Learning (PPML), where privacy of data is
guaranteed. In this thesis, we design an efficient platform, MPCLeague, for
PPML in the Secure Outsourced Computation (SOC) setting using Secure
Multi-party Computation (MPC) techniques.
  MPC, the holy-grail problem of secure distributed computing, enables a set of
n mutually distrusting parties to perform joint computation on their private
inputs in a way that no coalition of t parties can learn more information than
the output (privacy) or affect the true output of the computation
(correctness). While MPC, in general, has been a subject of extensive research,
the area of MPC with a small number of parties has drawn popularity of late
mainly due to its application to real-time scenarios, efficiency and
simplicity. This thesis focuses on designing efficient MPC frameworks for 2, 3
and 4 parties, with at most one corruption and supports ring structures.
  At the heart of this thesis are four frameworks - ASTRA, SWIFT, Tetrad,
ABY2.0 - catered to different settings. The practicality of our framework is
argued through improvements in the benchmarking of widely used ML algorithms --
Linear Regression, Logistic Regression, Neural Networks, and Support Vector
Machines. We propose two variants for each of our frameworks, with one variant
aiming to minimise the execution time while the other focuses on the monetary
cost. The concrete efficiency gains of our frameworks coupled with the stronger
security guarantee of robustness make our platform an ideal choice for a
real-time deployment of PPML techniques.
