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DOI: 10.14569/IJACSA.2022.0130111
PDF

Special Negative Database (SNDB) for Protecting Privacy in Big Data

Author 1: Tamer Abdel Latif Ali
Author 2: Mohamed Helmy Khafagy
Author 3: Mohamed Hassan Farrag

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 1, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Despite the importance of big data, it faces many challenges. The most important big data challenges are data storage, heterogeneity, inconsistency, timeliness, security, scalability, visualization, fault tolerance, and privacy. This paper concentrates on privacy which is one of the most pressing issues with big data. As mentioned in the Literature Review below there are numerous methods for safeguarding privacy with big data. This paper introduces an efficient technique called Specialized Negative Database (SNDB) for protecting privacy in big data. SNDB is proposed to avoid the drawbacks of all previous techniques. SNDB is based on deceiving bad users and hackers by replacing only sensitive attribute with its complement. Bad user cannot differentiate between the original data and the data after applying this technique.

Keywords: Big data; big data challenges; privacy violations; privacy-preserving techniques; special negative database; data integrity

Tamer Abdel Latif Ali, Mohamed Helmy Khafagy and Mohamed Hassan Farrag, “Special Negative Database (SNDB) for Protecting Privacy in Big Data” International Journal of Advanced Computer Science and Applications(IJACSA), 13(1), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130111

@article{Ali2022,
title = {Special Negative Database (SNDB) for Protecting Privacy in Big Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130111},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130111},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {1},
author = {Tamer Abdel Latif Ali and Mohamed Helmy Khafagy and Mohamed Hassan Farrag}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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