  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.
