AVEC: Bootstrapping Privacy for Local LLMs
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912585382625280 |
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| author | Gaikwad, Madhava |
| author_facet | Gaikwad, Madhava |
| contents | 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 Rényi 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_10561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AVEC: Bootstrapping Privacy for Local LLMs Gaikwad, Madhava Cryptography and Security Artificial Intelligence 68Q25, 68T50, 68P27 F.2.2; I.2.7; K.4.1 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 Rényi 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. |
| title | AVEC: Bootstrapping Privacy for Local LLMs |
| topic | Cryptography and Security Artificial Intelligence 68Q25, 68T50, 68P27 F.2.2; I.2.7; K.4.1 |
| url | https://arxiv.org/abs/2509.10561 |