Collaborative environments, particularly in data-sharing and cooperative settings, require robust mechanisms to fairly allocate rewards among participants. In many scenarios, participants contribute valuable data that improve the collective outcome, yet ensuring that each party is fairly compensated for their contribution remains a complex challenge. Classical cooperative game theory develops mechanisms under the assumption of non-replicable resources, which does not fit the scenario of sharing models or data. Hence, it does not address the potential imbalance in reciprocal benefits among pairs of participants, which can lead to strategic exploitation and, consequently, unfair reward allocations. We explicate fairness w.r.t. balanced reciprocity with an axiom and prove the existence of a reward allocation mechanism that fulfills this axiom, as well as other well-known axioms intended to capture incentivization and fairness. By emphasizing mutual fairness, our approach mitigates the risk of exploitation, fosters stable cooperation, and aligns with the principle of fair exchange. We propose a solution that guarantees proportional benefit allocation while maximizing group welfare under these constraints.