  Protecting sensitive information is crucial in today's world of Large
Language Models (LLMs) and data-driven services. One common method used to
preserve privacy is by using data perturbation techniques to reduce
overreaching utility of (sensitive) Personal Identifiable Information (PII)
data while maintaining its statistical and semantic properties. Data
perturbation methods often result in significant information loss, making them
impractical for use. In this paper, we propose 'Life of PII', a novel
Obfuscation Transformer framework for transforming PII into faux-PII while
preserving the original information, intent, and context as much as possible.
Our approach includes an API to interface with the given document, a
configuration-based obfuscator, and a model based on the Transformer
architecture, which has shown high context preservation and performance in
natural language processing tasks and LLMs.
  Our Transformer-based approach learns mapping between the original PII and
its transformed faux-PII representation, which we call "obfuscated" data. Our
experiments demonstrate that our method, called Life of PII, outperforms
traditional data perturbation techniques in terms of both utility preservation
and privacy protection. We show that our approach can effectively reduce
utility loss while preserving the original information, offering greater
flexibility in the trade-off between privacy protection and data utility. Our
work provides a solution for protecting PII in various real-world applications.
