Solving the Privacy-Equity Trade-off in Data Sharing By Using Homophily, Diversity, and t-Closeness Based Anonymity Algorithm

Solving the Privacy-Equity Trade-off in Data Sharing By Using Homophily, Diversity, and t-Closeness Based Anonymity Algorithm

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In the modern era, personal data published by data owners play a vital role in decision-making, resource allocation, disease mitigation, and/or epidemiological analysis.However, if the published data do not truly AEG DLE0970M 90cm Designer Island Hood – CHROME reflect the characteristics of the underlying population from all perspectives, informed decisions cannot be made, leading to disproportionally fewer benefits for some marginal populations.Unfortunately, existing anonymization techniques often dilute/erase the representation of various populations, especially minor and super-minor groups, in the anonymized data, which inadvertently propagates inequity in subsequently published data analytics.

To address these technical problems, in this paper, we implement a homophily, diversity- and t-closeness-based anonymity algorithm that effectively solves the privacy-equity trade-off (i.e., preserving privacy while representing all population groups, regardless of major/minor status) in anonymized data.

We implement an automated method to identify equity-vulnerable attributes from the original data to protect the values against dilution/deletion.We develop a clustering method that considers both homophily and diversity among records, and that constructs compact, diverse, and balanced clusters.We employed the t-closeness principle to ensure a balanced distribution of equity-vulnerable attribute values in all clusters.

We implement a flexible generalization scheme that performs only the required generalization of attributes to keep functional relationships similar between anonymized and real data.Rigorous experiments are performed on seven real-life benchmark datasets to justify the feasibility of our algorithm.Compared with the state-of-the-art method, our algorithm can lower privacy risks by up to 41.

98% and FOREST DRY SPRAY DEODROANT enhance data quality by up to 36.72%.From an equity preservation point of view, it shows a 28.

43% improvement over its counterpart, and equity losses in most cases are marginally lower than the original data.