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

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.

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}
}