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A Competitiveness-driven and Secure Incentive Mechanism for Competitive Organizations Data Sharing: A Contract Theoretic Approach

机译:竞争力的竞争力组织的竞争力和安全的激励机制数据分享:合同理论方法

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In the era of big data and artificial intelligence, data sharing is desirable for vigorous development of data-driven intelligent services. Although data sharing is supported to a certain extent by current mechanisms and technologies, organizations especially with potential competitive relationships might refuse to share their data. One reason is that data holders worry that data sharing improves competitors' competitiveness. The other reason is that data sharing suffers huge privacy security risk. To address these problems, in this paper, the concept of competitiveness is introduced as a data sharing transaction driving force to eliminate the competitiveness worry of data holders while differential privacy is adopt to protect their privacy. As there is an information asymmetry between data sharers and data demanders, a contract theoretic approach is proposed to motivate data holders to share data with privacy protection, which is expected to achieve a target of win-win and data sharing security. By designing optimal contracts, the data demander can decide rationally how to pay the data holders given the privacy parameter. Moreover, data holders can choose the contract that maximize their utilities. Numerical results substantiate the effectiveness of the the proposed scheme.
机译:在大数据和人工智能的时代,数据共享是可蓬勃发展的数据驱动智能服务的蓬勃发展。虽然通过当前机制和技术支持某种程度的数据共享,但尤其具有潜在的竞争关系,可能会拒绝分享他们的数据。一个原因是数据持有者担心数据分享提高了竞争对手的竞争力。另一个原因是数据分享遭受了巨大的隐私安全风险。为了解决这些问题,在本文中,竞争力的概念被引入作为数据共享交易驱动力,以消除数据持有者的竞争力担忧,而差异隐私是通过保护他们的隐私。由于数据共享者和数据要求之间存在的信息不对称,提出了一种合同理论方法,以激励数据持有者与隐私保护共享数据,这预计将实现双赢和数据共享安全性的目标。通过设计最佳合同,数据德拉德方案可以根据隐私参数授予如何支付数据持有者来决定。此外,数据持有人可以选择最大化其实用程序的合同。数值结果证实了该方案的有效性。

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