STRENGTHENING PRIVACY MEASURES IN DATA PUBLISHING: EXPLORING THE BIT-CODED-SENSITIVE ALGORITHM (BCSA) APPROACH

Abstract

Privacy-preserving data publishing (PPDP) is a crucial field of study that aims to safeguard individual privacy while enabling the release of useful information for data mining purposes. The widespread availability and exchange of large volumes of data in organizations necessitate the extraction of hidden information patterns for decision-making processes. However, this practice poses challenges to privacy and confidentiality. When organizations publish substantial amounts of data, there is a risk of exposing sensitive information, resulting in privacy violations. Therefore, it is imperative to protect information and restrict access to authorized individuals. This paper addresses the limitations of existing methods for achieving PPDP by proposing a novel technique called the "Bit-coded-sensitive algorithm" (BCSA).Existing approaches, such as k-anonymity, aim to anonymize datasets by removing key attributes that can be linked to individuals. However, simply removing attributes does not guarantee anonymity. The BCSA algorithm overcomes this limitation by efficiently and effectively preserving individual privacy. It can be combined with other algorithms, enhancing its resistance against adversarial attacks. This paper presents a comprehensive framework that encompasses k-anonymity in publishing data, previous work, the problem definition, the BCSA algorithm, and a conclusion.The proposed BCSA algorithm addresses the drawbacks of other anonymization methods, including grouping-and-breaking, suppression, generalization, bucketization, and perturbation. It ensures that membership disclosure is prevented, establishes a clear distinction between quasi and sensitive attributes, and maintains the link between these attributes. By incorporating the BCSA algorithm, organizations can achieve better data utility while upholding privacy requirements. The presented framework offers a valuable contribution to the field of PPDP and serves as a foundation for future research and advancements