A Decision Support Tool to Evaluate Data Anonymization Software
Open AccessEvaluation of data anonymization software confirmed the disconnect between data anonymization researchers on one hand and data anonymization software developers on the other hand. This disconnect represents a major risk for individual privacy that could only be addressed by bridging the gap between the two. Validation and analysis findings suggest that organizations, which often lack data anonymization inhouse expertise, would be well served to evaluate data anonymization software applications using Data Anonymization Decision Support Tool (DADST) to lower the risk of data re-identification attacks and information leakage. Failure to combat data re-identification attacks such as record linkage attacks can lead to monetary loss and a greater risk of reputation damage to organizations as has widely been reported in prior literature. This research introduces a Data Anonymization Decision Support Tool (DADST) to help organizations evaluate and rank data anonymization software applications using a novel performance indicator (PI) metric calculated based on well-established data anonymization privacy models introduced in prior literature, namely k-anonymity and ℓ-diversity. It aims to strike a balance between privacy preservation represented by anonymity level and diversity level, and information preservation represented by data distortion level. PI combines individual measures using simple additive weighting (SAW) to allow for the ability to compensate among individual measures. It has a constant value range between 0 and 1 to ensure accurate ranking in different contexts/scenarios. Testing and validation of PI metric showcased its robustness and reasonableness for helping organizations rank and select an effective data anonymization software among a list of candidate tools.
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