Abstract: Numerous governmental entities, including hospitals and the Bureau of Statistics, as well as other functional units, have shown great interest in personalized privacy. Numerous models and techniques for data posting have been put forward, the majority of which concentrated on a single sensitive property. A few scholarly articles highlighted the need to protect the privacy of data which includes many sensitive qualities. Utilizing current techniques like the sanctity of privacy in data gets decreased if many sensitive values are published while maintaining k-anonymity and l-diversity simultaneously. Furthermore, customization hasn't been investigated in this context. We describe a publishing strategy in this research that handles customization when publishing material that has many sensitive features for analysis. The model makes use of a slicing strategy that is reinforced by fuzzy approaches for numerical sensitive characteristics based on variety, generalization of categorical sensitive attributes, and probabilistic anonymization of quasi-identifiers using differential privacy. We limit the confidence that an adversary may draw about a sensitive value in a publicly available data collection to the level of understanding as an inference drawn from known information. Both artificial datasets based on real-life healthcare data were used in the trials. The outcomes guarantee that the data value is maintained while securing individual’s privacy.

Supriya G Purohit and Veeragangadhara Swamy. “Data Sensitivity Preservation-Securing Value Using Varied Differential Privacy Method (SP-SV Method)”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150782

@article{Purohit2024,
title = {Data Sensitivity Preservation-Securing Value Using Varied Differential Privacy Method (SP-SV Method)},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150782},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150782},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Supriya G Purohit and Veeragangadhara Swamy}
}