Data sharing across multiple different entities is on-demand to upgrade an enterprise's performance. However, some malicious entity can reveal this data to an unauthorised third party that may result in heavy loss to the enterprises in terms of finance, reputation, and long-term stability. This study presents a novel model GUIM-SMD for the identification of the guilty entity which is responsible for the data leakage to the unauthorised party in the shared environment. An effective distribution strategy to allocate the data among the users based on the access control mechanism is proposed in this model. The approach introduces the summation matrix which is computed using D-score and U-score that are assigned to the classified data and user, correspondingly. Furthermore, D-score and U-score are based on the data sensitivity and user guilty record relatively; and their values vary between 0 and 1. The evaluated summation matrix is used for data distribution among various users. The results show improvement up to 98.74, 236.38, and 252.39% for average probability, average success rate, and detection rate, respectively, as compared to the prior work.

中文翻译：

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GUIM-SMD：使用基于求和矩阵的分布的有罪用户识别模型

必须在多个不同实体之间共享数据，以提升企业的绩效。但是，某些恶意实体可以将该数据泄露给未经授权的第三方，这可能导致企业在财务，声誉和长期稳定性方面遭受重大损失。这项研究提出了一种新颖的模型GUIM-SMD，用于识别有罪实体，该实体负责将数据泄露给共享环境中的未授权方。该模型提出了一种基于访问控制机制的有效数据分配策略。该方法引入求和矩阵，该求和矩阵使用分别分配给分类数据和用户的D分数和U分数计算得出。此外，D分数和U分数是基于数据敏感性和用户有罪记录的；它们的值在0到1之间变化。评估的求和矩阵用于在各个用户之间进行数据分配。结果表明，与先前的工作相比，平均概率，平均成功率和检测率分别提高了98.74％，236.38和252.39％。