A Hybrid Secure Cloud Platform Maintenance Based on Improved Attribute-Based Encryption Strategies

Kumar, Abhishek; Kumar, Swarn Avinash; Dutt, Vishal; Dubey, Ashutosh Kumar; Narang, Sushil Kumar

doi:10.9781/ijimai.2021.11.004

Cited by 18 publications

(4 citation statements)

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“…The drawback in their research is the need for an arranged position of string whose size grows quadratically with the number of users and linearly growing running time for registration. Kumar et al [24] have introduced a hybrid model for an improved attribute-based encryption scheme for data security. This model is derived from two algorithms Advanced Encryption Standard (AES) algorithm and the Attribute-Based algorithm (ABE), which were more attractive and efficient for cloud data security and also provided a high level of security for service providers.…”

Section: Related Workmentioning

confidence: 99%

Secure Cloud Data with Attribute-based Honey Encryption

Siyal,

Long

2024

Preprint

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Encryption is a Technique to convert plain text into Cipher text, which is unreadable without an appropriate decryption key. Hadoop is a platform to process and store huge amounts of data reliably. It also provides a flexible service for big data through HDFS storage. Inappropriately, Hadoop's lack of built-in security measures makes it more likely that hostile attacks will be made against the data processed or stored using Hadoop. So, the data stored in Hadoop is a big, challenging task. The prevalence of private data attacks underscores the critical need for robust Encryption techniques. In this article, we have used Attribute-based encryption based on Cipher text policy attributes encryption. Attribute-based Honey Encryption (ABHE) is a novel encryption technique that provides an additional layer of security to data stored in a database. This paper especially focused on the file sizes encoded inside the Hadoop. Hadoop is good for handling big data but it lacks security, making data vulnerable to attacks. To fix this, we have created Attribute Honey Encryption (ABHE), a method to encode and decode files securely in HDFS, and compared work with the CP-ABE algorithm and KP-ABE algorithm. ABHE works well even with large files and performs better. This makes Hadoop safer for storing and processing data.

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Section: Related Workmentioning

confidence: 99%

Secure Cloud Data with Attribute-based Honey Encryption

Siyal,

Long

2024

Preprint

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“…Another area of focus is the development of ABE-based access control frameworks that can be used in different IoT domains, such as smart homes, smart cities, and industrial IoT. The work can also be extended by adding concepts such as deep learning and artificial intelligence, which will train the machine to protect the system against security threats and will help to increase overall accuracy and robustness [102]. By addressing these areas of focus, the use of ABE can be enhanced and optimized for secure and efficient IoT deployments.…”

Section: Future Workmentioning

confidence: 99%

Attribute-Based Encryption Schemes for Next Generation Wireless IoT Networks: A Comprehensive Survey

Shruti

Rani

Sah

et al. 2023

Sensors

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Most data nowadays are stored in the cloud; therefore, cloud computing and its extension—fog computing—are the most in-demand services at the present time. Cloud and fog computing platforms are largely used by Internet of Things (IoT) applications where various mobile devices, end users, PCs, and smart objects are connected to each other via the internet. IoT applications are common in several application areas, such as healthcare, smart cities, industries, logistics, agriculture, and many more. Due to this, there is an increasing need for new security and privacy techniques, with attribute-based encryption (ABE) being the most effective among them. ABE provides fine-grained access control, enables secure storage of data on unreliable storage, and is flexible enough to be used in different systems. In this paper, we survey ABE schemes, their features, methodologies, benefits/drawbacks, attacks on ABE, and how ABE can be used with IoT and its applications. This survey reviews ABE models suitable for IoT platforms, taking into account the desired features and characteristics. We also discuss various performance indicators used for ABE and how they affect efficiency. Furthermore, some selected schemes are analyzed through simulation to compare their efficiency in terms of different performance indicators. As a result, we find that some schemes simultaneously perform well in one or two performance indicators, whereas none shines in all of them at once. The work will help researchers identify the characteristics of different ABE schemes quickly and recognize whether they are suitable for specific IoT applications. Future work that may be helpful for ABE is also discussed.

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“…Abhishek Kumar et al [8] proposed a hybrid data security scheme called the Improved Attribute-Based Encryption Scheme (IABES). This model used two encryption algorithms, namely the Advanced Encryption Standard (AES) and the Attribute-Based Encryption (ABE) algorithm.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Container Allocation via Optimized Task Scheduling Through Deep Learning (OTS-DL) And High-Level Security

2023

KSII TIIS

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Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

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A Hybrid Secure Cloud Platform Maintenance Based on Improved Attribute-Based Encryption Strategies

Cited by 18 publications

References 0 publications

Secure Cloud Data with Attribute-based Honey Encryption

Secure Cloud Data with Attribute-based Honey Encryption

Attribute-Based Encryption Schemes for Next Generation Wireless IoT Networks: A Comprehensive Survey

Long-Term Container Allocation via Optimized Task Scheduling Through Deep Learning (OTS-DL) And High-Level Security

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