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Privacy preservation is crucial to protect sensitive information in the era of data-driven services. One common approach is to use data perturbation methods to reduce the utility of the data while preserving its statistical and semantic properties. These methods usually introduce significant information loss, limiting their practical use. In this paper, we propose Obfuscation Transformer which deals with PII information, a novel framework for transforming Personally Identifiable Information (PII) into faux-PII while preserving the original information, intent, and context as much as possible. Our approach consists of an API to interface and interact with given document, configuration based obfuscater and an a model based on the Transformer architecture, which has shown high degree of context preservation leading to higher performance and accuracy in natural language processing tasks and LLMs. 

We use the Transformer to learn a mapping between the original PII and a transformed faux-PII representation, which we term the "obfuscated" data. Our experiments demonstrate that the Life of PII outperforms traditional data perturbation methods in terms of both utility preservation and privacy protection. Specifically, we show that our approach can effectively reduce the utility loss while preserving the original information, achieving greater flexibility in trade-off between privacy protection and data utility. Our work provides a solution for protecting PII in various real-world applications. Wrap the entire output in JSON format.
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