ECCC : Edge Code Cloak Coder for Privacy Code Agent (2025)

Abstract: Large language models (LLMs) have significantly advanced automated code generation and debugging, facilitating powerful multi-agent coding frameworks. However, deploying these sophisticated models on resource-constrained edge devices remains challenging due to high computational demands, limited adaptability, and significant privacy risks associated with cloud-based processing. Motivated by these constraints, we propose Edge Code Cloak Coder (ECCC) , a novel edge-cloud hybrid framework integrating lightweight quantized LLM with robust AST-based anonymization and edge-side privacy validation. ECCC enables high-performance, privacy-preserving LLM capabilities on consumer GPUs, anonymizing user code before securely delegating abstracted tasks to cloud LLMs. Experimental evaluations demonstrate that ECCC achieves competitive correctness (within 4–5pp of the GPT-4-based frameworks) and a perfect privacy score of 10/10, effectively balancing functionality and security for sensitive and proprietary code applications.

## introduction large language models (llms) exhibit strong capabilities in code understanding, generation, and reasoning, catalyzing rapid progress in multi-agent frameworks exemplified by code agents (huang * corresponding author: hehq23@mail2.sysu.edu.cn @xcite . such systems typically coordinate roles including a programmer agent, test designer, test executor, and debugger, forming an automatic generate-verifyrepair loop that efficiently solves complex programming tasks. however, efficiently and trustworthily deploying powerful llms-especially large-parameter models-on resource-constrained edge devices (e.g., personal workstations and small business servers) faces three key challenges: to democratize the advancements of llms for all user groups, we propose a novel approach that addresses the above challenges comprehensively: we introduce edge code cloak coder (eccc), an innovative hybrid edge-cloud agent framework leveraging a lightweight, quantized open source llm to enable efficient deployment on edge devices (e.g., a single rtx 3090). the key innovation of eccc lies in its robust privacy protection abstraction layer, known as the privacy shield, implemented entirely on the edge device. crucially, only this anonymized abstract code is transmitted to powerful cloud-based llms (such as deepseek-v3 @xcite ) for logic enhancement or bug correction. the cloud returns anonymized code modifications without ever receiving identifiable user-specific symbols, effectively maintaining privacy. local edge devices subsequently handle de-anonymization and deterministic testing, ensuring that sensitive identifiers never traverse the network. ## methodology edge code cloak coder (eccc) executes a fourstage edge-cloud pipeline that keeps raw source code private while exploiting the reasoning strength of large cloud llms. the system follows a multistage pipeline as illustrated in fig. 1 , designed to integrate general-purpose generation, privacy protection, and semantic-level validation under a lightweight and locally executable architecture. the algorithmic details are described below. ## experiment we evaluate eccc with local privacy agent, ds-coder-v2-lite-instruct and ds v3-chat api @xcite . local inference will consume roughly up to 16 gb of gpu memory. we first elaborate the experimental setup, and then measure the code capability and privacy guard of eccc, respectively. ## conclusion this work introduces eccc, an edge-cloud agent framework. experiments show that eccc keeps pass@1 within 4-5pp of gpt-4-based agent stacks while achieving a perfect 10/10 privacy score and preserving output cleanliness. the results verify that lightweight quantitation, frozen backbones and deterministic de-anonymisation together provide a practical path for "every edge programmer" to harness large-scale reasoning without surrendering source-code secrecy.