Privacy and Data Protection in ChatGPT and Other AI Chatbots: Strategies for Securing User Information

Abstract

The evolution of artificial intelligence (AI) and machine learning (ML) has led to the development of sophisticated large language models (LLMs) that are used extensively in applications such as chatbots. This research investigates the critical issues of data protection and privacy enhancement in the context of LLM-based chatbots, with a focus on OpenAI's ChatGPT. It explores the dual challenges of safeguarding sensitive user information while ensuring the efficiency of machine learning models. It assesses existing Privacy-Enhancing Technologies (PETs) and proposes innovative methods, such as differential privacy, federated learning, and data minimization techniques. The study also includes a survey of Chatbot users to measure their concerns related to Data Privacy with the use of these LLM-based applications. This study is meant to serve as a comprehensive guide for developers, policymakers, and researchers, contributing to the discourse on data protection in artificial intelligence.

Keywords: ChatGPT, Large Language Model, LLMs, Privacy-Enhancing Technologies, Differentially Private Learning, Federated Learning, Data Minimization, Secure Multi-Party Computation, Data Protection, Artificial Intelligence, Cybersecurity