MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation

• URL: http://arxiv.org/abs/2506.20737v1
• Date: Wed, 25 Jun 2025 18:04:25 GMT
• Title: MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
• Authors: Gurusha Juneja, Alon Albalak, Wenyue Hua, William Yang Wang,
• Abstract summary: Existing benchmarks to evaluate contextual privacy in LLM-agents primarily assess single-turn, low-complexity tasks.<n>We first present a benchmark - MAGPIE comprising 158 real-life high-stakes scenarios across 15 domains.<n>We then evaluate the current state-of-the-art LLMs on their understanding of contextually private data and their ability to collaborate without violating user privacy.
• Score: 54.410825977390274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of LLM-based agents has led to increasing deployment of inter-agent collaboration for tasks like scheduling, negotiation, resource allocation etc. In such systems, privacy is critical, as agents often access proprietary tools and domain-specific databases requiring strict confidentiality. This paper examines whether LLM-based agents demonstrate an understanding of contextual privacy. And, if instructed, do these systems preserve inference time user privacy in non-adversarial multi-turn conversation. Existing benchmarks to evaluate contextual privacy in LLM-agents primarily assess single-turn, low-complexity tasks where private information can be easily excluded. We first present a benchmark - MAGPIE comprising 158 real-life high-stakes scenarios across 15 domains. These scenarios are designed such that complete exclusion of private data impedes task completion yet unrestricted information sharing could lead to substantial losses. We then evaluate the current state-of-the-art LLMs on (a) their understanding of contextually private data and (b) their ability to collaborate without violating user privacy. Empirical experiments demonstrate that current models, including GPT-4o and Claude-2.7-Sonnet, lack robust understanding of contextual privacy, misclassifying private data as shareable 25.2\% and 43.6\% of the time. In multi-turn conversations, these models disclose private information in 59.9\% and 50.5\% of cases even under explicit privacy instructions. Furthermore, multi-agent systems fail to complete tasks in 71\% of scenarios. These results underscore that current models are not aligned towards both contextual privacy preservation and collaborative task-solving.

Related papers

• Automated Privacy Information Annotation in Large Language Model Interactions [40.87806981624453]
  Users interacting with large language models (LLMs) under their real identifiers often unknowingly risk disclosing private information.<n>Existing privacy detection methods were designed for different objectives and application scenarios.<n>We construct a large-scale multilingual dataset with 249K user queries and 154K annotated privacy phrases.
  arXiv  Detail & Related papers  (2025-05-27T09:00:12Z)
• Multi-P$^2$A: A Multi-perspective Benchmark on Privacy Assessment for Large Vision-Language Models [65.2761254581209]
  We evaluate the privacy preservation capabilities of 21 open-source and 2 closed-source Large Vision-Language Models (LVLMs)<n>Based on Multi-P$2$A, we evaluate the privacy preservation capabilities of 21 open-source and 2 closed-source LVLMs.<n>Our results reveal that current LVLMs generally pose a high risk of facilitating privacy breaches.
  arXiv  Detail & Related papers  (2024-12-27T07:33:39Z)
• 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.<n>We instantiate PrivacyLens with a collection of privacy norms grounded in privacy literature and crowdsourced seeds.<n>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)
• LLM-PBE: Assessing Data Privacy in Large Language Models [111.58198436835036]
  Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs.
  arXiv  Detail & Related papers  (2024-08-23T01:37:29Z)
• 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)
• PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models [42.20437015301152]
  We present PrivLM-Bench, a benchmark for evaluating the privacy leakage of language models (LMs) Instead of only reporting DP parameters, PrivLM-Bench sheds light on the neglected inference data privacy during actual usage. We conduct extensive experiments on three datasets of GLUE for mainstream LMs.
  arXiv  Detail & Related papers  (2023-11-07T14:55:52Z)
• 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 Preserving Large Language Models: ChatGPT Case Study Based Vision and Framework [6.828884629694705]
  This article proposes the conceptual model called PrivChatGPT, a privacy-generative model for LLMs. PrivChatGPT consists of two main components i.e., preserving user privacy during the data curation/pre-processing together with preserving private context and the private training process for large-scale data.
  arXiv  Detail & Related papers  (2023-10-19T06:55:13Z)

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.