Privacy Preservation in Dynamic Data Through Synonymous Linkage on Micro Aggregation
M. Suresh Babu, G. Kathyayini, B. Md Irfan, Mohammed Raziuddin · 2024
The rise of the big data age and the growth of the mobile Internet and intelligent gadgets are undoubtedly responsible for the digitalization of personal information. However, the dissemination of such information raises the possibility of privacy violations. To address this issue, privacy preserving data publishing methods have been proposed. However, current methods based on anonymous models may not be effective in protecting non-numerical sensitive information that may contain synonymous linkages leading to privacy breaches. This paper proposes a microaggregation-based dynamic data publishing strategy to get around this restriction. The suggested approach adds a number of indicators to assess the relationships between values that are not numerically sensitive, which enhances the clustering impact of the microaggregation anonymous approach. In order to support the dynamic release and update of data, a dynamic update programme is also included. In comparison to current state-of-the-art methodologies, experimental research reveals that the suggested method offers greater privacy protection and publishable data availability. Therefore, the suggested microaggregation-based privacy-preserving dynamic data publishing strategy may provide a practical way to safeguard sensitive data while facilitating the sharing and publication of huge data.