Secure key management in cloud environment using quantum cryptography

Singamaneni, Kranthi Kumar; Naidu, P. Sanyasi

doi:10.3166/isi.23.5.213-222

Cited by 7 publications

(6 citation statements)

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“…is is often considered a critical methodology in cloud-based systems, structures, and research. Hash-based homomorphic encipherment validates the confidentiality of users' sensitive information in the directive of determining the protection back issue of storage space or administering users' confidential information by unauthorized parties [14,15,[25][26][27][28][29][30]. With the affordances of WWW, cloud consumers are capable of utilizing cloud customer services just about every time and in any place.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Singamaneni

Juneja²,

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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Topics such as computational sources and cloud-based transmission and security of big data have turned out to be a major new domain of exploration due to the exponential evolution of cloud-based data and grid facilities. Various categories of cloud services have been utilized more and more widely across a variety of fields like military, army systems, medical databases, and more, in order to manage data storage and resource calculations. Attribute-based encipherment (ABE) is one of the more efficient algorithms that leads to better consignment and safety of information located within such cloud-based storage amenities. Many outmoded ABE practices are useful for smaller datasets to produce fixed-size cryptograms with restricted computational properties, in which their characteristics are measured as evidence and stagnant standards used to generate the key, encipherment, and decipherment means alike. To surmount the existing problems with such limited methods, in this work, a dynamic nonlinear poly randomized quantum hash system is applied to enhance the safety of cloud-based information. In the proposed work, users’ attributes are guaranteed with the help of a dynamic nonlinear poly randomized equation to initialize the chaotic key, encipherment, and decipherment. In this standard, structured and unstructured big data from clinical datasets are utilized as inputs. Real-time simulated outcomes demonstrate that the stated standard has superior exactness, achieving over 90% accuracy with respect to bit change and over 95% accuracy with respect to dynamic key generation, encipherment time, and decipherment time compared to existing models from the field and literature. Experimental results are demonstrated that the proposed cloud security standard has a good efficiency in terms of key generation, encoding, and decoding process than the conventional methods in a cloud computing environment.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Singamaneni

Juneja²,

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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“…The vulnerability analysis using two prominent security tools, Nmap and Nessus is executed and presented by authors. Secure key management in cloud environment using quantum cryptography is presented by Kranthi and Sanyasi [31]. The proposed technique enhances the execution of the encryption/decoding procedure, and bolsters a more anchored information transmission process utilizing less computational time.…”

Section: Related Workmentioning

confidence: 99%

A Robust, Preference-Based Coordinator Election Algorithm for Distributed Systems

Supase,

Pansare

2023

ISI

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In peer-to-peer distributed systems, the selection of a reliable coordinator is a pivotal process, often vulnerable to node failure and communication link failure. Herein, we present an innovative Fault-Tolerant Coordinator Election Algorithm (FTCEA) designed to address these issues, specifically crafted to withstand node failures in peer-to-peer distributed systems. Our algorithm distinguishes itself by capitalizing on a unique preference-based method, which incorporates significant nodal attributes into the election process. This integration of nodal attributes contributes to the election of a durable and reliable coordinator, significantly enhancing the robustness of the system. A comprehensive analysis was conducted to measure FTCEA's communication complexity, execution time, and space complexity using a peer-to-peer distributed application. The results demonstrated that FTCEA successfully identifies a coordinator node with a communication cost of O(n) messages and a space complexity linear to the number of attributes, represented as O(n.m). Remarkably, FTCEA demonstrated an approximately 50.10% improvement in communication cost compared to the enhanced Bully algorithm, a widely utilized method in this domain. Moreover, FTCEA can maintain a linear storage cost of O(n), thereby significantly improving the computation cost. In summary, FTCEA offers a scalable and efficient solution for coordinator election in distributed systems, showing promising potential for practical applications in the field. The algorithm's unique design, robustness, and efficiency make it a valuable contribution to the advancement of peer-to-peer distributed systems.

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“…Ref. [27] presented a scheme for understanding complicated admittance management on enciphered text; by applying this scheme, the enciphered text could remain classified even though the third-party server is not trustable; additionally, this method has more confidence over the complicity occurrences by using users' attributes as IDs, while the enciphered text decides an access policy intended for the specific user to decipher. Ref.…”

Section: Conventional Hashing Standards On Cloud Environmentmentioning

confidence: 99%

An Efficient Hybrid QHCP-ABE Model to Improve Cloud Data Integrity and Confidentiality

Singamaneni

Nauman

Juneja³

et al. 2022

Electronics

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Cloud computational service is one of the renowned services utilized by employees, employers, and organizations collaboratively. It is accountable for data management and processing through virtual machines and is independent of end users’ system configurations. The usage of cloud systems is very simple and easy to organize. They can easily be integrated into various storages of the cloud and incorporated into almost all available software tools such as Hadoop, Informatica, DataStage, and OBIEE for the purpose of Extraction-Transform-Load (ETL), data processing, data reporting, and other related computations. Because of this low-cost-based cloud computational service model, cloud users can utilize the software and services, the implementation environment, storage, and other on-demand resources with a pay-per-use model. Cloud contributors across this world move all these cloud-based apps, software, and large volumes of data in the form of files and databases into enormous data centers. However, the main challenge is that cloud users cannot have direct control over the data stored at these data centers. They do not even know the integrity, confidentiality, level of security, and privacy of their sensitive data. This exceptional cloud property creates several different security disputes and challenges. To address these security challenges, we propose a novel Quantum Hash-centric Cipher Policy-Attribute-based Encipherment (QH-CPABE) framework to improve the security and privacy of the cloud user’s sensitive data. In our proposed model, we used both structured and unstructured big cloud clinical data as input so that the simulated experimental results conclude that the proposal has precise, resulting in approximately 92% correctness of bit hash change and approximately 96% correctness of chaotic dynamic key production, enciphered and deciphered time as compared with conventional standards from the literature.

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Secure key management in cloud environment using quantum cryptography

Cited by 7 publications

References 0 publications

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

A Robust, Preference-Based Coordinator Election Algorithm for Distributed Systems

An Efficient Hybrid QHCP-ABE Model to Improve Cloud Data Integrity and Confidentiality

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