Data sharing has become a key factor in data analytics, as it allows organizations to operate on others’ data for secondary usages. At the same time, when sharing datasets, it is essential to transform the data to protect sensitive information while still providing data consumers with high-quality access. This balance between privacy and visibility highlights the importance of quantifying privacy to ensure effective data sharing. Entropy, a key measure in information theory, is a natural fit for addressing the challenges of privacy quantification. However, current methods for measuring privacy, particularly those involving temporal and subjective data, face limitations in adaptability and scope. To address these issues, this paper proposes a Quantitative Privacy Evaluation Method Based on Tsallis Entropy for Trustworthy Data Sharing. This approach unifies existing entropy-based privacy models under a single Tsallis entropy framework, improving generalization across various datasets through an adjustable parameter. Building on this model, the paper introduces a method for quantifying the strength of privacy protection, providing theoretical support for assessing the degree of data sharing. Finally, the effectiveness of the proposed Tsallis entropy model in quantifying privacy is analyzed in the context of a multi-center clinical trial scenario.