in Game Theory, we introduce the data sharing process between a group of agents as a new class of cooperative games with modified definition of stability and fairness. Using these new definitions, we then theoretically study the optimal and suboptimal outcomes of such data sharing processes and their sensitivity to perturbation. Through experiments, we present intuitive insights regarding theoretical results analysed in this paper and discuss various ways in which data can be valued reasonably.\
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**Subject Descriptors:**\
Game Theory\
Mechanism Design\
Machine Learning\
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Keywords: Multi-party Machine Learning, Cooperative Games, Game Theory, Shapley Value, Characteristic Function

I would like to thank my advisor, Prof. Bryan Low, for guiding me throughout this project. He showed me numerous traits a good researcher should have and never hesitated to point me in the right direction whenever I appeared unsure of myself.

I would also like to thank my parents, who have always supported me in my endeavours. Lastly, I thank Ong Min for offering me many words of encouragement and being my emotional pillar of support throughout the years.

Introduction
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In recent years, there has been an increasing amount of work done in the field of collaborative 