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Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment
Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be enc...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319269/ https://www.ncbi.nlm.nih.gov/pubmed/37409083 http://dx.doi.org/10.7717/peerj-cs.1433 |
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author | Sun, Xiaoling Li, Shanshan |
author_facet | Sun, Xiaoling Li, Shanshan |
author_sort | Sun, Xiaoling |
collection | PubMed |
description | Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes. |
format | Online Article Text |
id | pubmed-10319269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103192692023-07-05 Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment Sun, Xiaoling Li, Shanshan PeerJ Comput Sci Adaptive and Self-Organizing Systems Presently, the focus of target detection is shifting towards the integration of information acquired from multiple sensors. When faced with a vast amount of data from various sensors, ensuring data security during transmission and storage in the cloud becomes a primary concern. Data files can be encrypted and stored in the cloud. When using data, the required data files can be returned through ciphertext retrieval, and then searchable encryption technology can be developed. However, the existing searchable encryption algorithms mainly ignore the data explosion problem in a cloud computing environment. The issue of authorised access under cloud computing has yet to be solved uniformly, resulting in a waste of computing power by data users when processing more and more data. Furthermore, to save computing resources, ECS (encrypted cloud storage) may only return a fragment of results in response to a search query, lacking a practical and universal verification mechanism. Therefore, this article proposes a lightweight, fine-grained searchable encryption scheme tailored to the cloud edge computing environment. We generate ciphertext and search trap gates for terminal devices based on bilinear pairs and introduce access policies to restrict ciphertext search permissions, which improves the efficiency of ciphertext generation and retrieval. This scheme allows for encryption and trapdoor calculation generation on auxiliary terminal devices, with complex calculations carried out on edge devices. The resulting method ensures secure data access, fast search in multi-sensor network tracking, and accelerates computing speed while maintaining data security. Ultimately, experimental comparisons and analyses demonstrate that the proposed method improves data retrieval efficiency by approximately 62%, reduces the storage overhead of the public key, ciphertext index, and verifiable searchable ciphertext by half, and effectively mitigates delays in data transmission and computation processes. PeerJ Inc. 2023-06-20 /pmc/articles/PMC10319269/ /pubmed/37409083 http://dx.doi.org/10.7717/peerj-cs.1433 Text en ©2023 Sun et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Adaptive and Self-Organizing Systems Sun, Xiaoling Li, Shanshan Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment |
title | Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment |
title_full | Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment |
title_fullStr | Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment |
title_full_unstemmed | Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment |
title_short | Multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment |
title_sort | multi-sensor network tracking research utilizing searchable encryption algorithm in the cloud computing environment |
topic | Adaptive and Self-Organizing Systems |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319269/ https://www.ncbi.nlm.nih.gov/pubmed/37409083 http://dx.doi.org/10.7717/peerj-cs.1433 |
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