Enabling PII Discovery in Textual Data via Outlier Detection

Discovering Personal Identifying Information (PII) in textual data is an important pre-processing step to enabling privacy preserving data analytics. One approach to PII discovery in textual data is to characterise the PII as abnormal or unusual observations that can potentially result in privacy violations. However, discovering PII in textual data is challenging because the data is unstructured, and comprises sparse representations of similar text elements. This limits the availability of labeled data for training and the accuracy of PII discovery. In this paper, we present an approach to discovering PII in textual data by characterising the PII as outliers. The PII discovery is done without labelled data, and the PII are identified using named entities. Based on the recognised named entities, we then employ five (5) unsupervised outlier detection models (LOF, DBSCAN, iForest, OCSVM, and SUOD). Our performance comparison results indicate that iForest offers the best prediction accuracy with an ROC AUC value of 0.89. We employ a masking mechanism, to replace discovered PII with semantically similar values. Our results indicate a median semantic similarity score of 0.461 between original and transformed texts which results in low information loss.