"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents

• URL: http://arxiv.org/abs/2309.11653v2
• Date: Tue, 2 Apr 2024 01:32:06 GMT
• Title: "It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents
• Authors: Zhiping Zhang, Michelle Jia, Hao-Ping Lee, Bingsheng Yao, Sauvik Das, Ada Lerner, Dakuo Wang, Tianshi Li,
• Abstract summary: The widespread use of Large Language Model (LLM)-based conversational agents (CAs) raises many privacy concerns. We analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs.
• Score: 27.480959048351973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users' perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs. However, users' erroneous mental models and the dark patterns in system design limited their awareness and comprehension of the privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, which complicated users' ability to navigate the trade-offs. We discuss practical design guidelines and the needs for paradigm shifts to protect the privacy of LLM-based CA users.

Related papers

• PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language Models [10.050972891318324]
  We propose a privacy preservation pipeline for protecting privacy and sensitive information during interactions between users and large language models. We construct SensitiveQA, the first privacy open-ended question-answering dataset. Our proposed solution employs a multi-stage strategy aimed at preemptively securing user information while simultaneously preserving the response quality of cloud-based LLMs.
  arXiv  Detail & Related papers  (2025-02-19T09:17:07Z)
• Unveiling User Preferences: A Knowledge Graph and LLM-Driven Approach for Conversational Recommendation [55.5687800992432]
  We propose a plug-and-play framework that synergizes Large Language Models (LLMs) and Knowledge Graphs (KGs) to unveil user preferences. This enables the LLM to transform KG entities into concise natural language descriptions, allowing them to comprehend domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-11-16T11:47:21Z)
• Rescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbots [2.2447085410328103]
  Rescriber is a browser extension that supports user-led data minimization in LLM-based conversational agents. Our studies showed that Rescriber helped users reduce unnecessary disclosure and addressed their privacy concerns. Our findings confirm the viability of smaller-LLM-powered, user-facing, on-device privacy controls.
  arXiv  Detail & Related papers  (2024-10-10T01:23:16Z)
• Secret Use of Large Language Model (LLM) [25.452542727215178]
  Large Language Models (LLMs) have decentralized the responsibility for the transparency of AI usage. Our study investigated the contexts and causes behind the secret use of LLMs. We found that such secretive behavior is often triggered by certain tasks, transcending demographic and personality differences among users.
  arXiv  Detail & Related papers  (2024-09-28T20:31:53Z)
• PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action [54.11479432110771]
  PrivacyLens is a novel framework designed to extend privacy-sensitive seeds into expressive vignettes and further into agent trajectories. We instantiate PrivacyLens with a collection of privacy norms grounded in privacy literature and crowdsourced seeds. State-of-the-art LMs, like GPT-4 and Llama-3-70B, leak sensitive information in 25.68% and 38.69% of cases, even when prompted with privacy-enhancing instructions.
  arXiv  Detail & Related papers  (2024-08-29T17:58:38Z)
• Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning [62.224804688233]
  differential privacy (DP) offers a promising solution by ensuring models are 'almost indistinguishable' with or without any particular privacy unit. We study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users.
  arXiv  Detail & Related papers  (2024-06-20T13:54:32Z)
• No Free Lunch Theorem for Privacy-Preserving LLM Inference [30.554456047738295]
  This study develops a framework for inferring privacy-protected Large Language Models (LLMs) It lays down a solid theoretical basis for examining the interplay between privacy preservation and utility.
  arXiv  Detail & Related papers  (2024-05-31T08:22:53Z)
• CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models [60.59638232596912]
  We introduce CLAMBER, a benchmark for evaluating large language models (LLMs) Building upon the taxonomy, we construct 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries.
  arXiv  Detail & Related papers  (2024-05-20T14:34:01Z)
• Human-Centered Privacy Research in the Age of Large Language Models [31.379232599019915]
  This SIG aims to bring together researchers with backgrounds in usable security and privacy, human-AI collaboration, NLP, or any other related domains to share their perspectives and experiences on this problem.
  arXiv  Detail & Related papers  (2024-02-03T02:32:45Z)
• Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory [82.7042006247124]
  We show that even the most capable AI models reveal private information in contexts that humans would not, 39% and 57% of the time, respectively. Our work underscores the immediate need to explore novel inference-time privacy-preserving approaches, based on reasoning and theory of mind.
  arXiv  Detail & Related papers  (2023-10-27T04:15:30Z)
• Privacy in Large Language Models: Attacks, Defenses and Future Directions [84.73301039987128]
  We analyze the current privacy attacks targeting large language models (LLMs) and categorize them according to the adversary's assumed capabilities. We present a detailed overview of prominent defense strategies that have been developed to counter these privacy attacks.
  arXiv  Detail & Related papers  (2023-10-16T13:23:54Z)

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.