End to End Data Security Challenges in Real-Time Drilling Data Environment - From Data Transfer to Analytics

Djamaluddin, Basirudin; Ferianto, Teguh; Akbar, Habibie

doi:10.2523/iptc-19723-ms

Cited by 2 publications

(1 citation statement)

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“…Additionally, the non-volatile nature of disk storage ensures data persistence without the need for continuous power, a critical consideration for archival data and applications where data integrity and availability are paramount (Harnsoongnoen, S., 2017). However, RAM's volatile nature presents a significant challenge, necessitating robust strategies for data persistence and recovery to safeguard against power failures or system crashes (Djamaluddin, B., Ferianto, T., & Akbar, H., 2020;Li, X., & Mao, Y., 2015). Furthermore, equipping servers with sufficient RAM to handle large datasets can be prohibitive, limiting the scalability of in-memory solutions for some organizations (Chen, Q., Hsu, M., & Zeller, H., 2011).…”

Section: Disk-based Analytics: a Bedrock Of Data Storagementioning

confidence: 99%

Assessing the Dynamics of Data Processing

2024

CEIS

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This study embarks on a comprehensive analysis to evaluate the efficiency and scalability of in-memory computing (IMC) compared to traditional disk-based processing for big data analytics. Utilising the "New York City Taxi Trip Duration" dataset from Kaggle, we designed an experiment focusing on three critical analytical tasks: aggregation, sorting, and filtering. Our objective was to quantify the performance improvements offered by IMC, as facilitated by Apache Spark, against conventional SQL queries executed on a disk-based system. The findings reveal that IMC consistently outperforms disk-based processing in execution time, with significant reductions observed across all tasks. Specifically, the aggregation task highlighted the stark contrast in data retrieval speed, demonstrating IMC's superior efficiency with a completion time of 47.3 seconds, compared to 138.7 seconds for disk-based processing. Similar disparities were noted in sorting and filtering tasks, further accentuating IMC's performance advantage. Resource utilisation analysis, focusing on CPU and RAM consumption, indicated higher demands associated with IMC, underscoring the trade-off between enhanced speed and increased resource usage. The investigation provides a nuanced understanding of the practical implications of adopting IMC for big data analytics, especially considering the resource constraints of home computing environments. By juxtaposing theoretical advantages with empirical data, this paper contributes to the ongoing discourse on optimising data processing methodologies in the era of big data, offering insights into the balance between computational efficiency and resource management.

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Section: Disk-based Analytics: a Bedrock Of Data Storagementioning

confidence: 99%

Assessing the Dynamics of Data Processing

2024

CEIS

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Cloud-edge data encryption in the internet of vehicles using Zeckendorf representation

Cai

2023

J Cloud Comp

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Cloud-edge data security is a key issue in the internet of vehicles (IoV), as the potential for data breaches increases as more vehicles are connected. As vehicles become smarter and more connected, the risk of unauthorized access to the data generated by the vehicles also increases. Data encryption is a highly effective security measure that is widely used to protect the IoV from malicious actors. By encrypting data, it becomes virtually impossible for unauthorized individuals to access the information. This ensures that only the intended parties can access the data, allowing for secure communication between cloud and edge. Data encryption is a cost-effective and reliable security measure that is essential for any organization that relies on the IoV. The IoV is characterized by the large volume of data that is exchanged between devices in cloud and edge. This necessitates the use of a strong encryption method, such as stream ciphering, which is particularly well-suited to this type of environment. Stream ciphering provides the highest levels of security, making it the ideal choice for securing data transmission in the IoV. Many stream ciphering algorithms use bitwise exclusive or (XOR) to encrypt the data stream, so the core is the generation of a pseudo-random key stream. This paper proves that the probability of the number 1 appearing in the middle part of the Zeckendorf representation is constant, which can be used to generate pseudo-random key stream sequences. The pseudo-random sequence generated by the linear feedback shift register (LSFR) is periodic, and the key sequence will be duplicated. The logistic chaos (LC) sequence is too sensitive to the disturbance of initial value, and its stability is poor. In this paper, our proposed ZPKG (key generator based on Zeckendorf presentation) algorithm solves these two main problems in stream ciphering. The generated key sequence not only has strong randomness, but also is infinitely long, and it is robust to the minor disturbance of the initial value. Graphical Abstract

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End to End Data Security Challenges in Real-Time Drilling Data Environment - From Data Transfer to Analytics

Cited by 2 publications

References 1 publication

Assessing the Dynamics of Data Processing

Assessing the Dynamics of Data Processing

Cloud-edge data encryption in the internet of vehicles using Zeckendorf representation

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