Privacy preserving NLP based on Entity Filtering and Searchable Encryption

Abstract. With chatbots gaining traction and their adoption growing in different verticals, e.g. Health, Banking, Dating; and users sharing more and more private information with chatbots — studies have started to highlight the privacy risks of chatbots. In this paper, we propose two privacy-preserving approaches for chatbot conversations. The first approach applies ‘entity’ based privacy filtering and transformation, and can be applied directly on the app (client) side. It however requires knowledge of the chatbot design to be enabled. We present a second scheme based on Searchable Encryption that is able to preserve user chat privacy, without requiring any knowledge of the chatbot design. Finally, we present some experimental results based on a real-life employee Help Desk chatbot that validates both the need and feasibility of the proposed approaches.

**The paper has been accepted for presentation at the **NeurIPS Joint Workshop on Privacy-preserving Machine Learning (PPML-PriML), 2020. (Paper preprint).

#encryption #artificial-intelligence #nlp #chatbots #privacy