ARANI: An Experiment Line-Based Approach for Privacy Preservation in Data Lakes

• Thiago Jordão Fluminense Federal University
• Marcos Bedo Fluminense Federal University
• Daniel de Oliveira Fluminense Federal University https://orcid.org/0000-0001-9346-7651

DOI: https://doi.org/10.5753/sbbd.2025.247756

Abstract

Data Lakes store large volumes of heterogeneous data, including sensitive information. Ensuring compliance with regulations such as the LGPD requires the use of anonymization techniques. Techniques applied in isolation, such as k-Anonymity or Differential Privacy, may be insufficient. Therefore, combining these techniques in configurable flows is essential. Experiment Lines enable the flexible structuring and instantiation of these flows. This paper proposes ARANI, an Experiment Line-based approach that allows the definition, execution, and evaluation of anonymization flows with support for multiple techniques.

Keywords: Security, Privacy, Anonimization, Data Lake, Experiment Line

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