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In previous work a novel Edge Lightweight Searchable Attribute-basedencryption (ELSA) method was proposed to support Industry 4.0 and specificallyIndustrial Internet of Things applications. In this paper, we aim to improveELSA by minimising the lookup table size and summarising the data records byintegrating Machine Learning (ML) methods suitable for execution at the edge.This integration will eliminate records of unnecessary data by evaluating addedvalue to further processing. Thus, resulting in the minimization of both thelookup table size, the cloud storage and the network traffic taking fulladvantage of the edge architecture benefits. We demonstrate our mini-ELSAexpanded method on a well-known power plant dataset. Our results demonstrate areduction of storage requirements by 21% while improving execution time by1.27x.

mini-ELSA: using Machine Learning to improve space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0