Anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations

Abstract

The growing dependence on data-driven methods in digital forensic investigations, particularly in sensitive sectors like healthcare, underscores the critical need to balance individual privacy with data utility. Traditional anonymisation techniques often degrade data quality, hampering effective forensic analysis. This research introduces the Extended Anonymisation Privacy Model (e-ANOP), a novel hybrid framework integrating generalization and suppression techniques to optimize the privacy-utility trade-off. Unlike conventional k-anonymity approaches, e-ANOP prioritizes the protection of sensitive attributes while preserving essential data patterns. Evaluated through a healthcare-based forensic investigation case study, e-ANOP demonstrated superior analytical integrity, maintaining high data utility without compromising privacy. These results highlight e-ANOP’s potential as a scalable, practical solution for privacy-preserving data analysis in digital forensics, offering significant advancements in safeguarding sensitive information while supporting robust investigative outcomes. The e-ANOP model addresses the limitations of existing anonymisation methods by introducing a dynamic, context-aware approach tailored to the complexities of digital forensic investigations. By leveraging adaptive generalization hierarchies and selective suppression, e-ANOP ensures that sensitive attributes—such as patient identifiers in healthcare datasets—are effectively anonymized while retaining critical patterns necessary for forensic analysis, such as temporal or behavioral trends. The model’s flexibility allows it to adapt to varying data structures and privacy requirements, making it applicable across diverse forensic scenarios. Furthermore, e-ANOP incorporates metrics to quantify both privacy preservation and data utility, enabling investigators to fine-tune the model based on specific case needs. Through rigorous testing on real-world healthcare datasets, e-ANOP achieved a significant reduction in re-identification risk while maintaining over 90% of the original data’s analytical value, positioning it as a robust tool for privacy-conscious digital forensics in high-stakes environments.