CON-QA: Privacy-Preserving QA using cloud LLMs in Contract Domain

• URL: http://arxiv.org/abs/2509.19925v1
• Date: Wed, 24 Sep 2025 09:29:17 GMT
• Title: CON-QA: Privacy-Preserving QA using cloud LLMs in Contract Domain
• Abstract summary: CON-QA is a hybrid privacy-preserving framework for secure question answering over enterprise contracts.<n>It operates through three stages: (i) semantic query decomposition, (ii) anonymization of detected sensitive entities, and (iii) anonymized response generation by a cloud-based LLM.<n>To rigorously evaluate CON-QA, we introduce CUAD-QA, a corpus of 85k question-answer pairs generated over 510 real-world CUAD contract documents.
• Score: 0.9957569102803299
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As enterprises increasingly integrate cloud-based large language models (LLMs) such as ChatGPT and Gemini into their legal document workflows, protecting sensitive contractual information - including Personally Identifiable Information (PII) and commercially sensitive clauses - has emerged as a critical challenge. In this work, we propose CON-QA, a hybrid privacy-preserving framework designed specifically for secure question answering over enterprise contracts, effectively combining local and cloud-hosted LLMs. The CON-QA framework operates through three stages: (i) semantic query decomposition and query-aware document chunk retrieval using a locally deployed LLM analysis, (ii) anonymization of detected sensitive entities via a structured one-to-many mapping scheme, ensuring semantic coherence while preventing cross-session entity inference attacks, and (iii) anonymized response generation by a cloud-based LLM, with accurate reconstruction of the original answer locally using a session-consistent many-to-one reverse mapping. To rigorously evaluate CON-QA, we introduce CUAD-QA, a corpus of 85k question-answer pairs generated over 510 real-world CUAD contract documents, encompassing simple, complex, and summarization-style queries. Empirical evaluations, complemented by detailed human assessments, confirm that CON-QA effectively maintains both privacy and utility, preserves answer quality, maintains fidelity to legal clause semantics, and significantly mitigates privacy risks, demonstrating its practical suitability for secure, enterprise-level contract documents.

Related papers

• CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement [50.91045324738942]
  CLAIM is an uncertainty-driven framework for active clarification learning in open-domain settings.<n>We propose an entropy-driven synthetic data generation pipeline that integrates uncertainty estimation with semantic clustering and reasoning-based judgments.<n> Experimental results demonstrate that CLAIM can learn stable and generalizable clarification strategies without relying on manually labeled data.
  arXiv  Detail & Related papers  (2026-08-12T04:27:45Z)
• Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation [50.614874098786515]
  We recast privacy preserving query rewriting under Contextual Integrity.<n>We propose a CI-guided reinforcement learning framework that converts essential and non-essential sensitive spans into verifiable optimization signals.<n> Experiments show that our learned rewriter achieves the best privacy-utility tradeoff, achieving up to +10.1 average utility over on-device baselines.
  arXiv  Detail & Related papers  (2026-06-02T14:28:28Z)
• CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering [8.946002046630845]
  CYBERMASKQA is a privacy-aware QA benchmark covering key security domains.<n> Generated through a systematic pipeline, the dataset combines human-curated base scenarios with LLM-driven semantic expansion.<n> Evaluations of QA accuracy and masking performance demonstrate the benchmark's utility.
  arXiv  Detail & Related papers  (2026-05-23T22:57:13Z)
• FERA: Uncertainty-Aware Federated Reasoning for Large Language Models [60.52562148874846]
  We study federated reasoning, where a server improves multi-step reasoning by coordinating with heterogeneous clients holding private demonstrations.<n>The key challenge is that client reliability is query-dependent, while the server cannot inspect client data to determine which contributions are trustworthy.<n>We propose Uncertainty-Aware Federated Reasoning (FERA), a training-free framework based on iterative server-client co-refinement.
  arXiv  Detail & Related papers  (2026-05-11T07:04:51Z)
• Securing the Agent: Vendor-Neutral, Multitenant Enterprise Retrieval and Tool Use [0.0]
  We introduce a layered isolation architecture combining policy-aware ingestion, retrieval-time gating, and shared inference.<n>This approach centralizes security-critical operations--tool execution authorization, state isolation, and policy enforcement--on the server.<n>We evaluate it empirically and show that ABAC gating eliminates cross-tenant leakage while introducing negligible overhead.
  arXiv  Detail & Related papers  (2026-05-06T17:59:21Z)
• Anonymous-by-Construction: An LLM-Driven Framework for Privacy-Preserving Text [0.42821598129654453]
  We develop an on-premise, LLM-driven substitution pipeline that anonymizes text.<n>We measure privacy, semantic utility, and trainability under privacy.<n>Our method attains state-of-the-art privacy, minimal topical drift, strong factual utility, and low trainability loss.
  arXiv  Detail & Related papers  (2026-03-17T23:46:15Z)
• Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption [25.87368479678027]
  RAG has emerged as a key technique for enhancing response quality of LLMs without high computational cost.<n>In traditional architectures, RAG services are provided by a single entity that hosts the dataset within a trusted local environment.<n>This dependence on untrusted third-party services introduces privacy risks.<n>We propose an efficient privacy-preserving RAG framework (ppRAG) tailored for untrusted cloud environments.
  arXiv  Detail & Related papers  (2026-01-18T09:29:50Z)
• Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systems [54.916243942641444]
  Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications.<n>We study an edge-cloud-expert cascaded LLM-based knowledge system that supports decision-making through a question-and-answer pipeline.
  arXiv  Detail & Related papers  (2025-12-23T03:10:09Z)
• Local Hybrid Retrieval-Augmented Document QA [0.0]
  Organizations handling sensitive documents face a critical dilemma: adopt cloud-based AI systems but compromise data privacy, or maintain local processing that ensures security but delivers poor accuracy.<n>We present a question-answering system that resolves this trade-off by combining semantic understanding with keyword precision, operating entirely on local infrastructure without internet access.
  arXiv  Detail & Related papers  (2025-11-13T13:34:32Z)
• Semantically-Aware LLM Agent to Enhance Privacy in Conversational AI Services [0.0]
  We present a semantically-aware privacy agent designed to safeguard sensitive PII data when using remote Large Language Models (LLMs)<n>Unlike prior work that often degrade response quality, our approach dynamically replaces sensitive PII entities in user prompts with semantically consistent pseudonyms.<n>Our results show that LOPSIDED reduces semantic utility errors by a factor of 5 compared to baseline techniques.
  arXiv  Detail & Related papers  (2025-10-30T21:34:23Z)
• Analyzing and Internalizing Complex Policy Documents for LLM Agents [53.14898416858099]
  Large Language Model (LLM)-based agentic systems rely on in-context policy documents encoding diverse business rules.<n>This motivates developing internalization methods that embed policy documents into model priors while preserving performance.<n>We introduce CC-Gen, an agentic benchmark generator with Controllable Complexity across four levels.
  arXiv  Detail & Related papers  (2025-10-13T16:30:07Z)
• The Sum Leaks More Than Its Parts: Compositional Privacy Risks and Mitigations in Multi-Agent Collaboration [72.33801123508145]
  Large language models (LLMs) are integral to multi-agent systems.<n>Privacy risks emerge that extend beyond memorization, direct inference, or single-turn evaluations.<n>In particular, seemingly innocuous responses, when composed across interactions, can cumulatively enable adversaries to recover sensitive information.
  arXiv  Detail & Related papers  (2025-09-16T16:57:25Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements [12.634151250093483]
  We introduce PAKTON: a fully open-source, end-to-end, multi-agent framework with plug-and-play capabilities.<n>PAKTON is designed to handle the complexities of contract analysis through collaborative agent and a novel retrieval-augmented generation (RAG) component.<n>Experiments demonstrate that PAKTON outperforms both general-purpose and pretrained models in predictive accuracy, retrieval performance, explainability, completeness, and grounded justifications.
  arXiv  Detail & Related papers  (2025-05-31T15:38:21Z)
• 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.<n>We construct SensitiveQA, the first privacy open-ended question-answering dataset.<n>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)
• Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning [58.57194301645823]
  Large language models (LLMs) are increasingly integrated into real-world personalized applications.<n>The valuable and often proprietary nature of the knowledge bases used in RAG introduces the risk of unauthorized usage by adversaries.<n>Existing methods that can be generalized as watermarking techniques to protect these knowledge bases typically involve poisoning or backdoor attacks.<n>We propose name for harmless' copyright protection of knowledge bases.
  arXiv  Detail & Related papers  (2025-02-10T09:15:56Z)
• Aligning Large Language Models for Faithful Integrity Against Opposing Argument [71.33552795870544]
  Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks.<n>They can be easily misled by unfaithful arguments during conversations, even when their original statements are correct.<n>We propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation.
  arXiv  Detail & Related papers  (2025-01-02T16:38:21Z)
• Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions [0.0]
  This paper introduces the Privacy-Preserving Zero-Shot Learning (PP-ZSL) framework, a novel approach leveraging large language models (LLMs) in a zero-shot learning mode.<n>Unlike conventional machine learning methods, PP-ZSL eliminates the need for local training on sensitive data by utilizing pre-trained LLMs to generate responses directly.<n>The framework incorporates real-time data anonymization to redact or mask sensitive information, retrieval-augmented generation (RAG) for domain-specific query resolution, and robust post-processing to ensure compliance with regulatory standards.
  arXiv  Detail & Related papers  (2024-12-10T17:20:47Z)
• Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering [48.43453390717167]
  We present and tackle the problem of Embodied Question Answering with Situational Queries (S-EQA) in a household environment.<n>Unlike prior EQA work, situational queries require the agent to correctly identify multiple object-states and reach a consensus on their states for an answer.<n>We introduce a novel Prompt-Generate-Evaluate scheme that wraps around an LLM's output to generate unique situational queries and corresponding consensus object information.
  arXiv  Detail & Related papers  (2024-05-08T00:45:20Z)

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.