Privacy-preserving anomaly detection in the cloud for quality assured decision-making in smart cities

Alabdulatif, A, Khalil, I, Kumarage, H, Zomaya, A and Yi, X 2019, 'Privacy-preserving anomaly detection in the cloud for quality assured decision-making in smart cities', Journal of Parallel and Distributed Computing, vol. 127, pp. 209-223.


Document type: Journal Article
Collection: Journal Articles

Title Privacy-preserving anomaly detection in the cloud for quality assured decision-making in smart cities
Author(s) Alabdulatif, A
Khalil, I
Kumarage, H
Zomaya, A
Yi, X
Year 2019
Journal name Journal of Parallel and Distributed Computing
Volume number 127
Start page 209
End page 223
Total pages 15
Publisher Academic Press
Abstract Rapid urbanisation places extensive demands on city services and infrastructure that mandate innovative and sustainable solutions which increasingly involve streamlined monitoring, collection, storage and analysis of massive, heterogeneous data. Analytics services, such as anomaly detection, work to both extract knowledge and support decision-making mechanisms that enable smart functionality over such contexts. However, data privacy and data quality remain significant challenges to assuring the quality of decision-making. This paper introduces a scalable, cloud-based model to provide a privacy preserving anomaly detection service for quality assured decision-making in smart cities. Homomorphic encryption is employed to preserve data privacy during the analysis and MapReduce based distribution of tasks and parallelisation is used to overcome computational overheads associated with homomorphic encryption. Experiments demonstrate that a high level of accuracy is maintained for anomaly detection performed on encrypted data with the adopted distributed data processing approach significantly reducing associated computational overheads.
Subject Computer System Security
Keyword(s) Anomaly detection
Cloud computing
Fully homomorphic encryption
Secure data analysis
Smart cities
DOI - identifier 10.1016/j.jpdc.2017.12.011
Copyright notice © 2018 Elsevier Inc. All rights reserved.
ISSN 0743-7315
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