ANALYSING SENSITIVE DATA SECURITY USING TOKENIZATION-BASED DATA MASKING IN THE EXTRACT-TRANSFORM-LOAD (ETL) PROCESS - Wayan Dana; Abdul Rahman (Information Technology and Science (ITScience), 2026)
Growth of internet users in Indonesia by 8.7% in 2025, reaching 212 million or 74.6% of the total population, has created significant opportunities for the digital economy but also raised concerns regarding data security. Inadequate management of digital data poses risks to public privacy, prompting the Indonesian government to enact Law Number 27 of 2022 on Personal Data Protection (UU PDP) as a legal framework for data protection. Effective data security measures are necessary across various stages, including safeguarding data during the Extract-Transform-Load (ETL) process. This study aims to develop a data masking technique based on tokenization during the transformation stage of the ETL process to enhance the security of sensitive data. The ETL process, which involves extracting data from diverse sources, transforming it into the required format, and loading it into a database, is particularly vulnerable to sensitive data exposure during the transformation stage, especially in unprotected staging environments. Tokenization replaces original data with tokens that hold no intrinsic value, ensuring data confidentiality throughout the transformation and staging phases. The study's findings indicate that tokenization effectively protects sensitive data before it is loaded into the database, while also minimizing the need for duplicate tables or additional storage for masked data. This research contributes practically to supporting the implementation of the UU PDP, strengthening data security in ETL systems, and fostering a secure digital ecosystem in Indonesia.