Fair division deals with the distribution of welfare among a population of agents with the goal of achieving fairness. We propose a new framework based on epistemic fairness through information withholding. The broad goal of this proposal is to provide axiomatic and algorithmic solutions for fair division in practical, large-scale, settings, as a broad contribution to the grand scheme of AI and economics for social good. In addition, this proposal plans to investigate the strategic behavior of agents under information withholding, develop mechanisms that prevent such strategic manipulations, and experimentally study the perception of fairness among humans.