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Top1. Introduction
“ChatGPT, developed by OpenAI in November 2022, is an AI chatbot that utilizes the Generative Pre-trained Transformer (GPT) model. OpenAI is an AI research and development company known for its innovative approaches in natural language processing. The GPT model, based on the Transformer architecture introduced by Vaswani et al. (2017), is trained on extensive datasets to generate contextually relevant and accurate responses to text-based inputs. However, as these systems become more sophisticated and widely used, concerns regarding user privacy and data protection have emerged. Large Language Models (LLMs) like ChatGPT aim to understand and generate human language, but their reliance on extensive datasets, which may contain sensitive information, raises privacy concerns. There is a risk of inadvertently capturing and exposing sensitive user data, particularly in the context of chatbots and virtual assistants where personal or confidential information is often disclosed. These concerns have been addressed in various research papers discussing the usage of LLM-based chatbots, such as those by Hariri (2023), Sebastian (2023), and Cao et al. (2023).
However, these research papers have not delved deeply into the topic of data privacy risks in LLM chatbots, this paper addresses this research gap by reviewing the data privacy risks associated with LLM chatbots. Further, to mitigate these privacy concerns, it is essential to develop effective strategies and technologies that can safeguard user data while maintaining the utility of LLMs. This paper aims to address this need by examining current privacy concerns, exploring existing privacy-enhancing technologies (PETs), and proposing novel techniques to ensure robust data protection in LLMs like ChatGPT. The techniques include differential privacy, federated learning, data minimization, and secure multi-party computation. Additionally, this research explores legal and ethical frameworks that can guide the responsible development of AI systems, considering both the tremendous potential of LLMs and the importance of user privacy. The paper serves as a comprehensive guide for developers, policymakers, and researchers in this rapidly evolving field, contributing to the ongoing dialogue about data protection in AI and promoting the development of innovative technologies that prioritize user privacy.”
1.1 Brief Overview of ChatGPT
ChatGPT is an advanced AI model developed by OpenAI, which utilizes the Generative Pretrained Transformer (GPT) series of models. GPT belongs to the category of large language models (LLMs), which are characterized by their extensive training on diverse and comprehensive linguistic datasets and their ability to generate human-like text that is contextually relevant and coherent. ChatGPT, specifically, is designed to engage in conversation with users, with applications ranging from virtual assistants and customer service bots to AI tutors and more. The power of ChatGPT lies in its capacity to understand and generate meaningful responses to a wide array of prompts, demonstrating a deep grasp of syntax, semantics, and even nuanced aspects of conversation such as humor and emotion. The ChatGPT is a closed model without information about its training dataset and how it is currently being trained. Preventing data leakage (training-test contamination) is one of the most fundamental principles of Machine learning because such leakage makes evaluation results unreliable (Aiyappa, Rachith, et al.,2023).
Training an LLM like ChatGPT involves two main steps: pre-training and fine-tuning (Zheng, Ou, et al.,2023). During pre-training, the model is exposed to a large corpus of Internet text to learn grammar, facts about the world, reasoning abilities, and unfortunately, some of the biases present in the training data. In the fine-tuning process, ChatGPT is further trained on a narrower dataset, generated with the help of human reviewers following specific guidelines provided by OpenAI. Despite its impressive capabilities, ChatGPT, like all AI systems, raises some important privacy and data protection issues. Since the model learns from vast amounts of data, there is a risk of it inadvertently learning and generating sensitive or personally identifiable information. Also, user interactions with ChatGPT could potentially expose personal data, either through the questions users ask or the context in which the system is deployed. Hence, it is critical to explore techniques and strategies to enhance privacy and data protection in ChatGPT and similar LLMs.