Widespread deployment of distributed machine learning algorithms has raised new privacy challenges. The focus of this paper is on improving privacy of each participant’s local information (such as dataset or loss function) while collaboratively learning underlying model. We present two iterative algorithms for privacy preserving distributed learning. Our algorithms involves adding structured randomization to the state estimates. We prove deterministic correctness (in every execution) of our algorithm despite the iterates being perturbed by non-zero mean random variables. We motivate privacy using privacy analysis of a special case of our algorithm referred to as Function Sharing strategy (presented in ).