论文标题
Mini-Elsa：使用机器学习来提高Edge轻巧可搜索属性的空间效率4.0
mini-ELSA: using Machine Learning to improve space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0
论文作者
论文摘要
在以前的工作中，提出了一种新型的Edge轻巧可搜索的基于属性的加密（ELSA）方法，以支持行业4.0，特别是工业互联网应用程序。在本文中，我们旨在通过整合适合在边缘执行的机器学习（ML）方法来最大程度地降低查找表大小并汇总数据记录来改善ELSA。这种集成将通过评估进一步处理的附加值来消除不必要数据的记录。因此，导致查找表尺寸，云存储和网络流量的最小化充分利用了边缘体系结构优势。我们证明了我们在著名的发电厂数据集上扩展的Mini-Elsa扩展方法。我们的结果表明，将存储要求减少了21％，同时将执行时间提高了1.27倍。
In previous work a novel Edge Lightweight Searchable Attribute-based encryption (ELSA) method was proposed to support Industry 4.0 and specifically Industrial Internet of Things applications. In this paper, we aim to improve ELSA by minimising the lookup table size and summarising the data records by integrating Machine Learning (ML) methods suitable for execution at the edge. This integration will eliminate records of unnecessary data by evaluating added value to further processing. Thus, resulting in the minimization of both the lookup table size, the cloud storage and the network traffic taking full advantage of the edge architecture benefits. We demonstrate our mini-ELSA expanded method on a well-known power plant dataset. Our results demonstrate a reduction of storage requirements by 21% while improving execution time by 1.27x.
