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Privacy is important considering the financial Domain as such data is highlyconfidential and sensitive. Natural Language Processing (NLP) techniques can beapplied for text classification and entity detection purposes in financialdomains such as customer feedback sentiment analysis, invoice entity detection,categorisation of financial documents by type etc. Due to the sensitive natureof such data, privacy measures need to be taken for handling and training largemodels with such data. In this work, we propose a contextualized transformer(BERT and RoBERTa) based text classification model integrated with privacyfeatures such as Differential Privacy (DP) and Federated Learning (FL). Wepresent how to privately train NLP models and desirable privacy-utilitytradeoffs and evaluate them on the Financial Phrase Bank dataset.

Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning