Privacy Preserving Chatbot Conversations

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 privacypreserving 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.