BlockExplorer: Exploring Blockchain Big Data Via Parallel Processing

Li, Shipeng; Li, Jingwei; Tang, Yuxing; Wang, Liu; He, Zheyuan; Li, Zihao; Cheng, Xueqing; Yang, Bai; Chen, Ting; Tang, Yuzhe; Liu, Zhe; Zhang, Xiaosong

doi:10.1109/tc.2023.3248280

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The delay required for serving cloud requests without attacks is a critical performance metric in the evaluation of security models for big data environments. In the provided data, we have compared four different models: STFM [5], DLBI [12], PPQCM [18], and the proposed model MAQSPBT. The delay values (D) are measured in milliseconds (ms) for different request loads, ranging from 45,000 to 450,000 requests.…”

Section: Results Analysis and Comparisonmentioning

confidence: 99%

“…Jitter obtained for serving requests under attacks is a critical metric for assessing the stability and predictability of performance in security models when faced with adversarial conditions. In this analysis, we compare four different models: STFM [5], DLBI [12], PPQCM [18], and the proposed model MAQSPBT. Jitter values (J) are measured in milliseconds (ms) for various request loads, ranging from 45,000 to 450,000 requests, with approximately 10% of these requests simulated as attack requests.…”

Section: Analysis Of the Model Under Attacksmentioning

confidence: 99%

“…The quest for robust cloud security mechanisms has been an ongoing endeavor since the inception of cloud computing operations. As the architecture and functionalities of cloud services have evolved, so have the security challenges and solutions [16,17,18]. Existing models have sought to address various aspects, from data integrity and confidentiality to secure access and trust levels [19,20].…”

Section: Literature Reviewmentioning

confidence: 99%

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IBMESR: Towards Next-Generation Big Data Security with An Integrated Blockchain Model for Efficient, Scalable, and Robust Operations

Tota,

Karmore

2023

Preprint

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As the utilization of big data environments becomes increasingly widespread, the demand for robust and efficient security mechanisms within this context becomes paramount, especially in real-time scenarios. Ensuring data integrity, trust, and availability in multi-cloud environments is of utmost importance. Existing solutions often fall short in these aspects, resulting in issues such as latency, energy inefficiency, and vulnerability to access attacks, among other drawbacks. Traditional security models designed for big data environments tend to rely on ineffective proof mechanisms and static encryption methods. These conventional approaches lead to increased data processing delays, heightened energy consumption, and reduced communication throughput. Furthermore, these existing models have demonstrated limited capabilities in accurately identifying unauthorized access attempts, thereby escalating security risks. This paper introduces an innovative security framework tailored for big data environments, which leverages a suite of cutting-edge technologies to address the limitations of current models. The proposed architecture integrates several key components, including a Proof of Spatial & Temporal Trust-based Blockchain for secure storage operations, Grey Wolf Optimization (GWO) for efficient blockchain sharding, Fully Homomorphic Encryption (FHE) for safeguarding data across various data nodes, Physical Unclonable Functions (PuFs) for resilient inter-data node communications, and Fuzzy Rule-Based Access Control to enhance overall security. Our research results demonstrate that this proposed model significantly mitigates the inefficiencies and vulnerabilities that currently plague big data security systems. Specifically, it achieves an 8.3% reduction in data processing delays, a 4.5% reduction in energy consumption, a 2.9% improvement in communication throughput, an 8.5% enhancement in the accuracy of detecting access attacks, and a 3.5% increase in access speed levels. This innovative security framework tailored for big data environments offers a comprehensive and effective approach to overcome the limitations of existing solutions. It represents a significant advancement in the field of big data security, contributing significantly to ongoing efforts aimed at creating more secure, efficient, and resilient big data ecosystems.

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Section: Results Analysis and Comparisonmentioning

confidence: 99%