Navigating the Privacy-Utility Tradeoff: An Interactive Methodology for Structured Tabular Data Publication
The publication of government data promotes transparency and supports advances in scientific research. Such publications may occur either mandatorily or upon request. However, published datasets may contain sensitive information about data subjects, potentially leading to privacy concerns. To address these issues, many countries have established regulations governing the handling of personal data. Nevertheless, these regulations generally do not specify the technical mechanisms that data managers should adopt when publishing data. The literature proposes several privacy-preserving models that can be employed to protect sensitive information during data publication. However, the application of these models often reduces data utility and may even render the published data unsuitable for certain purposes. Consequently, achieving an appropriate balance between privacy preservation and data utility remains a major challenge in data publication. This article proposes a methodology for privacy-preserving publication of structured tabular data under two scenarios: mandatory publication, in which the data manager is legally required to disclose the data, and requested publication, in which data are released upon request. In the requested publication scenario, the methodology introduces an interactive process through which data managers and requesters collaboratively negotiate the transformations applied to the data in order to balance privacy and utility. To evaluate the proposed methodology, a case study was conducted using a real-world dataset, including an assessment based on the Technology Acceptance Model (TAM). The results indicate that the methodology supports privacy-preserving data publication and enables a collaborative balance between privacy and utility through the proposed interactive process.