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A Novel Study of Different Privacy Frameworks Metrics and Patterns

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Advances in Distributed Computing and Machine Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 127))

Abstract

Learnability has impacted data privacy and security exposing two sides of a coin. A breach in security eventually leads to loss of privacy and vice versa. Evolution of technologies has put forth new platforms simplifying data derivation and assimilation providing information on the go. Even though different policies and metrics are in place, the objective varies along with the factors determined by technological advancement. This paper describes existing privacy metrics and patterns while providing an overall view of different mathematical framework privacy preserving. Furthermore, maintaining trust and utility becomes a challenge in preserving privacy and security as different techniques and technologies for assimilation of information are readily available without any restraints.

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Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, VIT, Vellore, Tamil Nadu, India

    Sukumar Rajendran

  2. Department of Software System and Engineering, School of Information Technology and Engineering, VIT, Vellore, Tamil Nadu, India

    J. Prabhu

Authors
  1. Sukumar Rajendran
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  2. J. Prabhu
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Corresponding author

Correspondence to J. Prabhu .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Asis Kumar Tripathy

  2. San Diego State University, San Diego, CA, USA

    Mahasweta Sarkar

  3. Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Suchismita Chinara

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Rajendran, S., Prabhu, J. (2021). A Novel Study of Different Privacy Frameworks Metrics and Patterns. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_19

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