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Privacy Preservation Technique Based on Sensitivity Levels for Multiple Numerical Sensitive Overlapped Attributes

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Hybrid Intelligent Systems (HIS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 420))

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Abstract

In today’s world, information is a hot topic to discuss and explore. We all strive to get more information and utilize it for advancement. Data is the main object to get information. Data is considered as a big data nowadays as it contains a massive amount of information. Knowing the benefits of knowledge does not mean an adversary cannot use the same information to harm an individual. Protecting data from the intrusion of an adversary can be done through Privacy Preserving Data Publishing Methods. When there is a combination of sensitive data in a data set that is correlated to each other, several models are not efficient for protecting data. We propose a novel model K-MNSSOA for preserving data privacy based on the concept of K-anonymity in this paper. An in-depth study of the K-MNSOA model for Privacy Preserving Data Publishing is presented in this article, which protects sensitive data privacy breaches even when the set of data has multiple overlapping sensitive numerical attributes. Using this model, levels of sensitivity are designed, and generalization is performed on overlapping attributes based on the sensitivity in sensitive attributes.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Oriental University, Indore, India

    Nidhi M. Chourey & Rashmi Soni

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  1. Nidhi M. Chourey
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  2. Rashmi Soni
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Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  3. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Vincenzo Piuri

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. University of Bari, Bari, Italy

    Gabriella Casalino

  6. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  7. Ontario Tech University, Oshawa, ON, Canada

    Patrick Hung

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Chourey, N.M., Soni, R. (2022). Privacy Preservation Technique Based on Sensitivity Levels for Multiple Numerical Sensitive Overlapped Attributes. In: Abraham, A., et al. Hybrid Intelligent Systems. HIS 2021. Lecture Notes in Networks and Systems, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-96305-7_5

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