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Adaptive Encryption Technique for Collaborative Cloud Environments

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Internet of Things and Analytics for Agriculture, Volume 2

Part of the book series: Studies in Big Data ((SBD,volume 67))

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Abstract

In recent times, the data storage in collaborative cloud environment has distorted a lot when compared with prior times. Security flattering is one of the major imperative challenges in database storage and classification. Data stored in databases are susceptible and thus focusing the safety only on the susceptible data that minimizes the delays or troubles in the classification. This paper describes an extremely innovative and novel methodology for securing numeric data in databases. It presents a realistic solution to the problem where numeric data are transformed into alphanumeric types and hence there is minimal probability of storing encrypted data in the existing numeric field. The proposed algorithm allows translucent verification intensity encryption that does not modify the data field category or the fixed time span.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SCAD College of Engineering and Technology, Cheranmadevi, India

    Y. Harold Robinson & A. Vegi Fernando

  2. Department of Computer Science and Engineering, Anna University Regional Campus, Tirunelveli, India

    E. Golden Julie

Authors
  1. Y. Harold Robinson
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  2. E. Golden Julie
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  3. A. Vegi Fernando
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Corresponding author

Correspondence to Y. Harold Robinson .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

  2. Department of Computer Science and Engineering, LNCT College, Jabalpur, Madhya Pradesh, India

    Raghvendra Kumar

  3. Department of Computer Science and Engineering, Brainware University, Kolkata, West Bengal, India

    Souvik Pal

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Harold Robinson, Y., Golden Julie, E., Vegi Fernando, A. (2020). Adaptive Encryption Technique for Collaborative Cloud Environments. In: Pattnaik, P., Kumar, R., Pal, S. (eds) Internet of Things and Analytics for Agriculture, Volume 2. Studies in Big Data, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-15-0663-5_5

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