Machine learning has started to be deployed in fields such as healthcare and finance, which involves dealing with a lot of sensitive data. This propelled the need for and growth of privacy-preserving machine learning (PPML). We propose an actively secure four-party protocol (4PC), and a framework for PPML, showcasing its applications on four of the most widely-known machine learning algorithms -Linear Regression, Logistic Regression, Neural Networks, and Convolutional Neural Networks. (Communication). Protocol Π BitExt (Fig. 19) requires 1 round and a communication of 4 + 1 bits in the offline phase, while it requires 3 rounds and a communication of 5 + 2 bits in the online phase.Proof: During the offline phase, parties execute one instance each of Π vSh and Π B vSh resulting in one round and a communication of + 1 bits (Lemma C.1). Also, the offline phase for multiplication is performed resulting in an additional communication of 3 bits.During the online phase, parties first execute an arithmetic multiplication, resulting in one round and communication of 3 bits (Lemma B.4). The value rv is reconstructed towards both P 0 and P 3 , resulting in an additional round and an amortized communication of 2 bits. This is followed by the last round, where parties execute one instance of Π B vSh resulting in a communication of 2 bits. Thus the online phase requires three rounds and an amortized communication of 5 + 2 bits.Lemma D.4 (Communication). Protocol ReLU (Π relu ) requires 3 rounds and a communication of 8 + 2 bits in the offline phase, while it requires 4 rounds and a communication of 8 +2 bits in the online phase.