A Low-Code Data Anonymization Platform for Achieving Data Privacy for Research Data

Abstract With the rapidly evolving digital technology landscape, characterized by cutting-edge technologies such as artificial intelligence, big data, and machine learning, data privacy is increasingly becoming a topic of global urgency in both policy and technical domains. It therefore calls for practical and context-sensitive anonymization strategies that enable researchers to protect sensitive information while preserving data utility for analysis and sharing. Despite the availability of numerous anonymization techniques, their practical adoption remains limited because existing tools often require advanced programming skills, are commercially restricted, or provide little guidance for selecting appropriate methods. Consequently, many researchers and institutions, particularly in resource-constrained settings, face difficulties implementing privacy-preserving data sharing. Existing anonymization software tends to be either prohibitively complex, commercially restricted, or not sensitive to resource-constrained research settings. In this paper, we introduce a novel Data Anonymization Platform designed to implement ethical data stewardship that is both usable and scalable, while adhering to high privacy standards. The platform integrates several anonymization methods, including suppression, masking, bucketing, pseudonymization, tokenization, and k-anonymity, among others, on a Shiny-based dashboard. It enables users to upload datasets and apply anonymization techniques through an intuitive point-and-click interface. It adapts the results based on the dataset’s structure and produces code in R, Stata, and Python to support reproducibility, transparency, and accountability. The platform then allows users to download the anonymized dataset upon completion. Unlike many existing anonymization tools that require advanced programming expertise or provide limited support for reproducible workflows, the platform combines an intuitive low-code interface, integrated disclosure-risk assessment, and automatic generation of reproducible R, Stata, and Python code within a single environment. By lowering technical barriers to advanced anonymization, it enables institutions in resource-constrained settings to implement privacy-preserving data sharing while aligning with international data governance standards. This platform is informed by the principles of data justice and equitable data governance, which emphasize fairness, inclusion, transparency, and the protection of individuals and communities throughout the data lifecycle. By lowering technical barriers to privacy-preserving data sharing, the platform seeks to reduce inequities in access to advanced anonymization technologies and promote context-sensitive anonymization practices, particularly for sensitive health, education, and community data in Africa. In doing so, it contributes to more trustworthy, accountable, and equitable data ecosystems.