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Do You Know Where Your Cloud Files Are? Improving Accuracy of Cloud Location Detection using Modified K-Medoid Algorithm

Affiliations

  • Charotar University of Science and Technology, Changa - 388421, Gujarat, India

Abstract


Background/Objectives: The emergence of Cloud computing in recent times as a globally accepted data and activity management platform is phenomenal, albeit with few clutches not only related with the data integrity and their privacy but also of their geographical location. The gravity of the issue arises with the plethora of guidelines and variations in legal procedures in storing the data and getting access to them in diverse geographical areas. The aim of present investigation is to figure-out a potentially effective method that can help detect a forged server with better location coordinates than the currently employed methods. Methods/Statistical Analysis: In this study we developed a made use of an established method to check the position of the files at a specified place as claimed by service provider. The main means employed during the course of this investigation is K-Medoid Algorithm for finding the server's geographical location putting emphasis on putative adverse outcome. Findings: The greatness of K-Medoid Algorithm is to detect a potentially forged location and also to identify the possible elements involved in forgery. This work involved the use of Tulip Dataset and Implementation in NS3 for validating algorithm modified by us to detect the forged location and possible location of involved adversaries. The investigation during this work culminated in proposing a modified K-Medoid algorithm for helping a user to find the Cloud file system which verifies the storage location with very high efficiency. Improvements/Applications: Through our research technique, user able to detect faked location and identify the dishonest service provider. Our work culminated in the best estimation of the location of server for a cloud service provider within a less distance than the previous work.

Keywords

Cloud Computing, Geolocation, K-Medoid, Trilateration.

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