Title: Data Pricing Mechanism Based on Property Rights Compensation Distribution. Abstract: While machine learning (ML) benefits from data, it also faces the challenges of ambiguous data ownership, including privacy violations and increased costs of using data. Yet existing approaches to data valuation may focus on preventing privacy breaches, but do not truly protect data ownership. This is because a data trading marketplace that protects data ownership should achieve this goal: once data is traded, its ownership does not transfer to a new owner but merely enlarges its coverage. Considering that the transfer of property rights in the process of data trading makes compensation necessary, this paper proposes the first data valuation mechanism based on modern property rights theory. Specifically, we propose the integration of property rights to improve the final revenue of the entire workflow called the “data chain” while compensating process executors who lost ownership after integration. Then, we consider the expectations of both the integrator and the integrated party during the compensation allocation. For the former, we apply compound interest to assess a total compensation equivalent to the time value for the Data chain. For the latter, we respect and meet their expectations as much as possible. To achieve this, we provide the framework based on Least-core to assign the compensation and prove that our framework can also work compared to existing algorithms. Finally, to cope with more complex situations, we adjust the traditional Least-core and demonstrate theoretically and experimentally that the compensation mechanism is feasible and effective in solving the data pricing problem.