Ciphertext Database Audit Technology Under Searchable Encryption Algorithm and Blockchain Technology

Jin, Quan

doi:10.4018/jgim.315014

Cited by 14 publications

(5 citation statements)

References 48 publications

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“…Collecting additional video data from a wider range of oyster reefs in the New York Harbor, as well as other estuarine ecosystems, can improve the model's generalization capabilities and increase its applicability to diverse environments. Additionally, future work should focus on refining the model, addressing the current limitations, and exploring new data collection and analysis techniques with emerging technologies, such as big data analytics (Bag et al, 2022;Xie et al, 2022;Xing et al, 2022), blockchain (Buthelezi et al, 2022;Harshvardhan & Teoh, 2022;Qiu, 2022), edge computing (Liang et al, 2022;Huang et al, 2021), the Internet of Things (Almomani et al, 2021;Peng et al, 2021), and deep learning (Wu et al, 2022;Zhang & Song, 2022;Zhao, 2022). By continuously improving and expanding upon this research, we can contribute to the advancement of oyster restoration efforts, enhance ecological management practices, and further our understanding of oyster populations and their vital role in estuarine ecosystems.…”

Section: Discussionmentioning

confidence: 99%

AI-Powered Tracking for Sustainable Marine Ecosystem Resource Management Projects

Chau,

Zhang,

Gui

et al. 2023

International Journal of Information Technology Project Management

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Ecosystems are our planet's life-support systems that facilitate sustainable development. Within the marine ecosystem, oysters serve as a keystone species. Numerous oyster restoration projects have been launched with a crucial element involving precise assessment of oyster population sizes within specific reef areas. However, the current methods of tracking oyster populations are approximate and lack precision. To address this research gap, the authors developed an AI-empowered project for oyster detection. Specifically, they created a dataset of wild oysters, utilized Roboflow for image annotation, and employed image augmentation techniques to augment the training data. Then, they fine-tuned a YOLOv8 computer vision object detection model using their dataset. The results demonstrated a mean average precision (mAP) of 85.2 percent and an accuracy of 87.7 percent for oyster detection. This approach improved upon previous attempts to detect wild oysters, offering a more effective solution for population assessment, which is a fundamental step toward sustainable oyster restoration project management.

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Section: Discussionmentioning

confidence: 99%

AI-Powered Tracking for Sustainable Marine Ecosystem Resource Management Projects

Chau,

Zhang,

Gui

et al. 2023

International Journal of Information Technology Project Management

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“…Blockchain technology adaptation keeps data in blocks and records it as a complete and discrete ledger (Nguyen and Kim, 2018; Liang et al ., 2022). A certain amount of information is stored in each block, which is connected in a chain following the order in which they were each created (Li et al ., 2020; Qiu, 2022). Blockchain technology adaptation has the attributes of trust and decentralization (Wang and Li, 2019).…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Blockchain technology adaptation and organizational inertia: moderating role between knowledge management processes and supply chain resilience

Zhou

Zhang

et al. 2023

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PurposeSupply chain resilience and knowledge management (KM) processes have received increasing attention from researchers and practitioners. Nevertheless, previous studies often treat the two streams of literature independently. Drawing on the knowledge-based theory, this study aims to reconcile these two different streams of literature and examine how and when KM processes influence supply chain resilience.Design/methodology/approachThis research develops a conceptual model to test a sample of data from 203 Chinese manufacturing firms using a structural equation modeling method. Specifically, the current study empirically examines how KM processes affect different forms of supply chain resilience (supply chain readiness, responsiveness and recovery) and examines the moderating effect of blockchain technology adaptation and organizational inertia on the relationship between KM processes and supply chain resilience.FindingsThe findings show that KM processes positively affect three dimensions of supply chain resilience, i.e., supply chain readiness, responsiveness and recovery. Besides, the study reveals that blockchain technology adoption positively moderates the relationships between KM processes and supply chain resilience, whereas organizational inertia negatively moderates these above relationships.Originality/valueThis research linked the two research areas of supply chain resilience and KM processes, further bridging the gap in the research exploration of KM in the supply chain field. Next, this study contributes to supply chain resilience research by investigating how KM systems positively impact supply chain readiness, responsiveness and recovery. In addition, this study found a moderating effect of blockchain technology adaption and organizational inertia on the relationship between KM processes and supply chain resilience. These findings provide a reference for Chinese manufacturing firms to strengthen supply chain resilience, achieve secure supply chain operations and gain a competitive advantage in the supply chain. This studys’findings advance the understanding of supply chain resilience and provide practical implications for supply chain managers.

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“…CC brings great ease by allowing applications and users to access diverse services remotely [1]. In addition to general users communicating with cloud storage (CS), the administrator's applications with private accounts frequently accomplish private activity to do particular tasks like downloading database files and deleting virtual hosts [2]. Cloud data are widely dispersed by cloud storage providers (CSP) on a cloud network.…”

Section: Introductionmentioning

confidence: 99%

Improved Sunflower Optimization Algorithm Based Encryption with Public Auditing Scheme in Secure Cloud Computing

2023

IJIES

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Cloud computing (CC) is a pivotal technology in the entire universe. Cloud platforms allow management applications or administrators with privileged accounts to remotely do privileged activities for particular tasks like eliminating virtual hosts. If private accounts can be leaked and perform a dangerous confidential activity, severe security issues are performed in cloud environments. To sort out these issues, researchers focused on auditing the behaviours of private users. But it was found to be tough to audit fine-grained privileged behaviours automatically for the graphical operating system. Also, it is difficult to prevent users from avoiding the audit mechanism or preventing hackers from attacking the audit method. Therefore, this study presents a new improved sunflower optimization algorithm based encryption with public auditing scheme (ISFOAE-PAS) in the secure cloud environment. The major intention of the ISFOAE-PAS technique lies in the accomplishment of data security, integrity, and auditing in the cloud environment. In addition, the ISFOAE-PAS technique designs a new secret image encryption using the homomorphic encryption (SIE-HE) technique to encrypt the data. Moreover, the ISFO algorithm is derived for optimally choosing the keys related to the SIE-HE technique. Furthermore, the public auditing scheme is designed to improve security in the cloud environment. Finally, the DDoS attack detection process is performed by a deep convolutional autoencoder (DCAE). For demonstrating the greater efficiency of the ISFOAE-PAS technique, a series of simulations were involved. The experimental values inferred that the ISFOAE-PAS technique reaches maximum accuracy of 99.42%.

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Ciphertext Database Audit Technology Under Searchable Encryption Algorithm and Blockchain Technology

Cited by 14 publications

References 48 publications

AI-Powered Tracking for Sustainable Marine Ecosystem Resource Management Projects

AI-Powered Tracking for Sustainable Marine Ecosystem Resource Management Projects

Blockchain technology adaptation and organizational inertia: moderating role between knowledge management processes and supply chain resilience

Improved Sunflower Optimization Algorithm Based Encryption with Public Auditing Scheme in Secure Cloud Computing

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