Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity
Twinkle Patel, Kiran R. Amin · International Journal of Computer Applications · 2024
In recent years, the proliferation of extensive personal data has sparked concerns over privacy infringement and data misuse.This data encompasses various facets of individuals' lives, including shopping patterns, criminal records, medical histories, and credit profiles.While the exchange and analysis of such data offer substantial benefits for businesses and governments, privacy apprehensions can hinder data sharing.To address these concerns, privacy-preserving data publishing techniques have emerged.Our approach focuses on p-sensitive kanonymity, a method that extends traditional k-anonymity to consider multiple sensitive attributes simultaneously.By anonymizing data in this manner, individuals' identities are protected, mitigating the risk of re-identification while still enabling meaningful analysis.Our proposed approach aims to strike a balance between data utility and privacy protection, facilitating informed decision-making without compromising individual privacy rights.