AVEC: Bootstrapping Privacy for Local LLMs

• URL: http://arxiv.org/abs/2509.10561v1
• Date: Wed, 10 Sep 2025 07:59:01 GMT
• Title: AVEC: Bootstrapping Privacy for Local LLMs
• Authors: Madhava Gaikwad,
• Abstract summary: AVEC is a framework for bootstrapping privacy for local language models.<n>It enforces privacy at the edge with explicit verifiability for delegated queries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This position paper presents AVEC (Adaptive Verifiable Edge Control), a framework for bootstrapping privacy for local language models by enforcing privacy at the edge with explicit verifiability for delegated queries. AVEC introduces an adaptive budgeting algorithm that allocates per-query differential privacy parameters based on sensitivity, local confidence, and historical usage, and uses verifiable transformation with on-device integrity checks. We formalize guarantees using R\'enyi differential privacy with odometer-based accounting, and establish utility ceilings, delegation-leakage bounds, and impossibility results for deterministic gating and hash-only certification. Our evaluation is simulation-based by design to study mechanism behavior and accounting; we do not claim deployment readiness or task-level utility with live LLMs. The contribution is a conceptual architecture and theoretical foundation that chart a pathway for empirical follow-up on privately bootstrapping local LLMs.

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