ProxyGPT: Enabling Anonymous Queries in AI Chatbots with (Un)Trustworthy Browser Proxies
- URL: http://arxiv.org/abs/2407.08792v1
- Date: Thu, 11 Jul 2024 18:08:04 GMT
- Title: ProxyGPT: Enabling Anonymous Queries in AI Chatbots with (Un)Trustworthy Browser Proxies
- Authors: Dzung Pham, Jade Sheffey, Chau Minh Pham, Amir Houmansadr,
- Abstract summary: We present ProxyGPT, a privacy-enhancing system that enables anonymous queries in popular chatbots platforms.
The system is designed to support key security properties such as content integrity via TLS-backed data provenance, end-to-end encryption, and anonymous payment.
Our human evaluation shows that ProxyGPT offers users a greater sense of privacy compared to traditional AI chatbots.
- Score: 12.552035175341894
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: AI-powered chatbots (ChatGPT, Claude, etc.) require users to create an account using their email and phone number, thereby linking their personally identifiable information to their conversational data and usage patterns. As these chatbots are increasingly being used for tasks involving sensitive information, privacy concerns have been raised about how chatbot providers handle user data. To address these concerns, we present ProxyGPT, a privacy-enhancing system that enables anonymous queries in popular chatbot platforms. ProxyGPT leverages volunteer proxies to submit user queries on their behalf, thus providing network-level anonymity for chatbot users. The system is designed to support key security properties such as content integrity via TLS-backed data provenance, end-to-end encryption, and anonymous payment, while also ensuring usability and sustainability. We provide a thorough analysis of the privacy, security, and integrity of our system and identify various future research directions, particularly in the area of private chatbot query synthesis. Our human evaluation shows that ProxyGPT offers users a greater sense of privacy compared to traditional AI chatbots, especially in scenarios where users are hesitant to share their identity with chatbot providers. Although our proof-of-concept has higher latency than popular chatbots, our human interview participants consider this to be an acceptable trade-off for anonymity. To the best of our knowledge, ProxyGPT is the first comprehensive proxy-based solution for privacy-preserving AI chatbots. Our codebase is available at https://github.com/dzungvpham/proxygpt.
Related papers
- Bots can Snoop: Uncovering and Mitigating Privacy Risks of Bots in Group Chats [2.835537619294564]
SnoopGuard is a group messaging protocol that ensures user privacy against chatbots while maintaining strong end-to-end security.
Our prototype implementation shows that sending a message in a group of 50 users takes about 30 milliseconds when integrated with Message Layer Security (MLS)
arXiv Detail & Related papers (2024-10-09T06:37:41Z) - Are LLM-based methods good enough for detecting unfair terms of service? [67.49487557224415]
Large language models (LLMs) are good at parsing long text-based documents.
We build a dataset consisting of 12 questions applied individually to a set of privacy policies.
Some open-source models are able to provide a higher accuracy compared to some commercial models.
arXiv Detail & Related papers (2024-08-24T09:26:59Z) - WildChat: 1M ChatGPT Interaction Logs in the Wild [88.05964311416717]
WildChat is a corpus of 1 million user-ChatGPT conversations, which consists of over 2.5 million interaction turns.
In addition to timestamped chat transcripts, we enrich the dataset with demographic data, including state, country, and hashed IP addresses.
arXiv Detail & Related papers (2024-05-02T17:00:02Z) - User Privacy Harms and Risks in Conversational AI: A Proposed Framework [1.8416014644193066]
This study identifies 9 privacy harms and 9 privacy risks in text-based interactions.
The aim is to offer developers, policymakers, and researchers a tool for responsible and secure implementation of conversational AI.
arXiv Detail & Related papers (2024-02-15T05:21:58Z) - Evaluating Chatbots to Promote Users' Trust -- Practices and Open
Problems [11.427175278545517]
This paper reviews current practices for testing chatbots.
It identifies gaps as open problems in pursuit of user trust.
It outlines a path forward to mitigate issues of trust related to service or product performance, user satisfaction and long-term unintended consequences for society.
arXiv Detail & Related papers (2023-09-09T22:40:30Z) - ChatGPT for Us: Preserving Data Privacy in ChatGPT via Dialogue Text
Ambiguation to Expand Mental Health Care Delivery [52.73936514734762]
ChatGPT has gained popularity for its ability to generate human-like dialogue.
Data-sensitive domains face challenges in using ChatGPT due to privacy and data-ownership concerns.
We propose a text ambiguation framework that preserves user privacy.
arXiv Detail & Related papers (2023-05-19T02:09:52Z) - FedBot: Enhancing Privacy in Chatbots with Federated Learning [0.0]
Federated Learning (FL) aims to protect data privacy through distributed learning methods that keep the data in its location.
The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training.
The system is specifically designed to improve its performance and accuracy over time by leveraging its ability to learn from previous interactions.
arXiv Detail & Related papers (2023-04-04T23:13:52Z) - Cross-Network Social User Embedding with Hybrid Differential Privacy
Guarantees [81.6471440778355]
We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way.
In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types.
To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
arXiv Detail & Related papers (2022-09-04T06:22:37Z) - CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement
Learning [60.348822346249854]
This study presents a framework whereby several empathetic chatbots are based on understanding users' implied feelings and replying empathetically for multiple dialogue turns.
We call these chatbots CheerBots. CheerBots can be retrieval-based or generative-based and were finetuned by deep reinforcement learning.
To respond in an empathetic way, we develop a simulating agent, a Conceptual Human Model, as aids for CheerBots in training with considerations on changes in user's emotional states in the future to arouse sympathy.
arXiv Detail & Related papers (2021-10-08T07:44:47Z) - Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn
Chatbot Responding with Intention [55.77218465471519]
This paper proposes an innovative framework to train chatbots to possess human-like intentions.
Our framework included a guiding robot and an interlocutor model that plays the role of humans.
We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
arXiv Detail & Related papers (2021-03-30T15:24:37Z) - Personalized Chatbot Trustworthiness Ratings [19.537492400265577]
We envision a personalized rating methodology for chatbots that relies on separate rating modules for each issue.
The method is independent of the specific trust issues and is parametric to the aggregation procedure.
arXiv Detail & Related papers (2020-05-13T22:42:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.