论文标题
BDSP：一个公平支持区块链的框架，用于隐私增强企业数据共享
BDSP: A Fair Blockchain-enabled Framework for Privacy-Enhanced Enterprise Data Sharing
论文作者
论文摘要
在整个行业中，组织与其客户，合作伙伴，供应商和内部团队之间的协作和创新的数据共享率不断增加。但是，由于不同地区的监管限制，移动大量数据的绩效问题或维持自主权的要求，许多企业受到自由共享数据的限制。在这种情况下，企业可以从联邦学习的概念中受益，在该概念上，在该概念上，在该概念上，在该概念上在各种地理网站上构建了机器学习模型。在本文中，我们介绍了一个总体框架，即BDSP，以基于区块链和联合学习技术的企业之间共享数据。具体而言，我们提出了透明度贡献会计机制，以估计数据的估值并实施概念验证以进行进一步评估。广泛的实验结果表明，与基线方法相比，拟议的BDSP具有较高的训练精度，较高的训练精度，增加了3％，较低的通信开销，降低了3次。
Across industries, there is an ever-increasing rate of data sharing for collaboration and innovation between organizations and their customers, partners, suppliers, and internal teams. However, many enterprises are restricted from freely sharing data due to regulatory restrictions across different regions, performance issues in moving large volume data, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning, in which machine learning models are constructed at various geographic sites. In this paper, we introduce a general framework, namely BDSP, to share data among enterprises based on Blockchain and federated learning techniques. Specifically, we propose a transparency contribution accounting mechanism to estimate the valuation of data and implement a proof-of-concept for further evaluation. The extensive experimental results show that the proposed BDSP has a competitive performance with higher training accuracy, an increase of over 5%, and lower communication overhead, reducing 3 times, compared to baseline approaches.
