Privacy Based Data Publishing Model for Cloud Computing Environment

Benifa, J. V. Bibal; Mini, G. Venifa

doi:10.1007/s11277-020-07320-3

Cited by 12 publications

(4 citation statements)

References 37 publications

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“…By anonymizing and encrypting an individual's personal information using solid passwords, companies can reduce the risk of external attacks. According www.ijacsa.thesai.org to [13], these encryption protocols can help a company manage its security measures while meeting regulatory requirements. The use of data handling procedures is also essential in this case.…”

Section: E Protecting Data and Meeting Regulations: Balancing Privacy...mentioning

confidence: 99%

Network Intrusion Detection in Cloud Environments: A Comparative Analysis of Approaches

Ahmadi

2024

IJACSA

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This research study comprehensively analyzes network intrusion detection in cloud environments by examining several approaches. These approaches have been explored and compared to determine the optimal and appropriate choice based on specific conditions. This research study employs a qualitative approach, specifically conducting a thematic literature analysis from 2020 to 2024. The research material has been exclusively obtained via Google Scholar. The traditional approaches identified in this research include anomaly-based and signaturebased detection, along with innovative technologies and methods such as user behavior monitoring and machine learning. The findings of these studies demonstrate the effectiveness of conventional methods in known threat detection. They also struggle to identify novel attacks and understand the need for hybrid approaches that integrate the strengths of both. In this research study, the authors have addressed challenges such as privacy compliance, performance scalability, and false positives, highlighting the importance of continuous monitoring, privacypreserving technologies, and real-time threat intelligence integration. This study also highlights the importance of stakeholder buy-in and staff training for the successful implementation of a network intrusion detection system (NIDS), especially when determining the evolving nature of cyber threats. This study concludes by defining a balanced approach combining new and old methodologies to offer an effective defense against diverse cyber threats in cloud environments. The future scope of NIDS in cloud environments has also been discussed, including enhancing privacy compliance capabilities and integrating AIdriven anomaly detection to meet emerging threats and regulatory requirements.

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Section: E Protecting Data and Meeting Regulations: Balancing Privacy...mentioning

confidence: 99%

Network Intrusion Detection in Cloud Environments: A Comparative Analysis of Approaches

Ahmadi

2024

IJACSA

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“…Bibal Benifa and Venifa Mini 19 introduced a Genetic Gray Wolf Optimization method for the preservation of private documents and maintaining the sensitive records of patients which are stored in the cloud platform. The protected records are selected for publication thus, the data are secured against the attackers who forged to view the sensitive documents.…”

Section: Literature Reviewmentioning

confidence: 99%

Quasi‐identifier recognition with echo chamber optimization‐based anonymization for privacy preservation of cloud storage

Jadhav,

Borkar

2023

Concurrency and Computation

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SummaryThe publication of data raises several security concerns, implying that when a reputable company offers data to a third party, personal information is not needed to be revealed. The fundamental issue that led to identity leakage is the connection of quasi‐identifiers (QIDs); however, the majority of researchers overlook the identification of accurate QIDs. This study developed a privacy preservation and clustering‐based quasi‐identification model based on the Echo Chamber optimization with a Z‐mixture parameter for privacy‐preserving combined data publishing to maintain the privacy of the data. The Echo chamber optimization is used to conduct a clustering‐based quasi‐identification to find the significant attributes especially, in determining the dimension of the solutions as well as the precise QIDs using the re‐identification risk rate as the fitness function. The loss of information is greatly reduced in terms of the metrics such as normalized certainty penalty metric, average equivalent class size metric, and discernibility metric. The developed optimized clustering‐based algorithm with the privacy preservation model extensively minimizes the leakage of private information and the utilization of data is well‐maintained compared with other existing algorithms.

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“…In 2020, Bibal et al 25 have employed k‐anonymization model for upgrading the privacy strategies in cloud storages. Along with this, the GGWO model was deployed that decided the data to be published depending on the data conserved for confidentiality purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Nevertheless, cluster similarity was formulated only for arithmetical data. GGWO algorithm used in Reference 25 offers minimal information losses with high utility, but it needs to exploit varied datasets for publishing phase.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Doss

Kamalakkannan

2022

Concurrency and Computation

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This work intends to introduce an optimization-based privacy preservation model via selecting the optimal key matrix. Here, privacy preservation is carried out under two processes, namely, "data sanitization and restoration." In fact, data sanitization is the data preservation method, where the data (message) are preserved using the optimal key. Similarly, data restoration is the inverse procedure of sanitization. Here, the key matrix is optimally chosen using a novel hybrid algorithm. For optimization purpose, this work deploys a hybrid optimization approach known as random-based grey dragon algorithm (R-GDA) that involves the concepts of both "dragonfly algorithm (DA) and grey wolf optimization (GWO) algorithm." The novelty of the work is introduced in hybrid optimization approach R-GDA. Eventually, the supremacy of the adopted method is validated over other existing approaches in terms of various measures such as privacy, utility, and so on. The privacy preservation in the cloud is achievable in the field of education, banking sector, military, and the research community.

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Privacy Based Data Publishing Model for Cloud Computing Environment

Cited by 12 publications

References 37 publications

Network Intrusion Detection in Cloud Environments: A Comparative Analysis of Approaches

Network Intrusion Detection in Cloud Environments: A Comparative Analysis of Approaches

Quasi‐identifier recognition with echo chamber optimization‐based anonymization for privacy preservation of cloud storage

Hybrid optimization‐based privacy preservation of database publishing in cloud environment

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