S
Sandeep Saxena
Researcher at Galgotia's College of Engineering and Technology
Publications - 21
Citations - 107
Sandeep Saxena is an academic researcher from Galgotia's College of Engineering and Technology. The author has contributed to research in topics: Cloud computing & Cloud computing security. The author has an hindex of 4, co-authored 20 publications receiving 46 citations. Previous affiliations of Sandeep Saxena include National Institute of Technology, Durgapur & Sharda University.
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A Formal Specification Smart-Contract Language for Legally Binding Decentralized Autonomous Organizations
TL;DR: In this paper, a smart-legal-contract markup language (SLCML) for legal and business constructs to define a legally-binding decentralized autonomous organizations (DAO) is proposed.
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CGP: Cluster-based gossip protocol for dynamic resource environment in cloud
TL;DR: In this paper, the authors proposed a framework and protocol for the resource environment in the cloud, which is adaptable, reliable and scalable and supports green computing by utilizing server solidification.
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An Evolutionary Study of Multi-Objective Workflow Scheduling in Cloud Computing
TL;DR: This paper characterize a comparative review of multiobjective workflow scheduling algorithms that are listed below and suggest ways to improve the quality of services and reduce the energy consumption, reduce the CO2 emission and enhance the performance of whole cloud system.
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SEAI: Secrecy and Efficiency Aware Inter-gNB Handover Authentication and Key Agreement Protocol in 5G Communication Network
TL;DR: In this article, the authors proposed Secrecy and Efficiency Aware Inter-gNB (SEAI) handover authentication and key agreement (AKA) protocol for 5G communication network.
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Preventing from Cross-VM Side-Channel Attack Using New Replacement Method
TL;DR: This paper tries to investigate the current state of side-channel vulnerabilities involving the central processing unit cache and identifies the shortcomings of earlier defenses in a Cloud environment through flush-reload based statistical techniques which exploit the vulnerabilities of Square and Multiply algorithm.