ELSA: a keyword-based searchable encryption for cloud-edge assisted industrial internet of things

Aljabri, J., Michala, A. L. and Singer, J. (2022) ELSA: a keyword-based searchable encryption for cloud-edge assisted industrial internet of things. In: 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2022), Taormina (Messina), Italy, 16-19 May 2022, ISBN 9781665499569 (doi: 10.1109/CCGrid54584.2022.00035)

Abstract

The Industrial Internet of Things (IIoT) plays a powerful role in smart manufacturing by performing real-time analysis for large volumes of data. In addition, IIoT systems can monitor several factors, such as data accuracy, network bandwidth and operations latency. To perform these operations securely and in a privacy-preserving manner, one solution is to use cryptographic primitives. However, most cryptographic solutions add performance overhead causing latency. In this paper, we propose an Edge Lightweight Searchable Attribute-based encryption system (ELSA). ELSA leverages the cloud-edge architecture to improve search time beyond the state-of-the-art. The main contributions of this paper are as follows. First, we present an untrusted cloud/trusted edge architecture, which optimises the efficiency of data processing and decision making in the IIoT context. Second, we enhance search performance over current state-of-the-art (LSABE-MA) by an order of magnitude. We achieve this by improving the organisation of the data to provide better than linear search performance. We leverage the edge server to cluster data indices by keyword and introduce a query optimiser. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for linear search. In addition, we achieve this without sacrificing accuracy over the results.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Aljabri, Jawhara Bader R and Michala, Dr Lito and Singer, Dr Jeremy
• Authors: Aljabri, J., Michala, A. L., and Singer, J.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Computing Science
• ISBN: 9781665499569
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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