mini-ELSA: using Machine Learning to improve space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0
This work addresses storage and efficiency challenges for Industry 4.0 and Industrial Internet of Things applications, but it is incremental as it builds on a prior method.
The paper tackled the problem of space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0 by integrating machine learning to minimize lookup table size and summarize data records, resulting in a 21% reduction in storage requirements and a 1.27x improvement in execution time.
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.