@inproceedings{he-etal-2025-eccc,
title = "{ECCC}: Edge Code Cloak Coder for Privacy Code Agent",
author = "He, Haoqi and
Xu, Wenzhi and
Liu, Ruoying and
Tang, Jiarui and
Li, Bairu and
Lin, Xiaokai",
editor = "Zhang, Chen and
Allaway, Emily and
Shen, Hua and
Miculicich, Lesly and
Li, Yinqiao and
M'hamdi, Meryem and
Limkonchotiwat, Peerat and
Bai, Richard He and
T.y.s.s., Santosh and
Han, Sophia Simeng and
Thapa, Surendrabikram and
Rim, Wiem Ben",
booktitle = "Proceedings of the 9th Widening NLP Workshop",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.winlp-main.14/",
doi = "10.18653/v1/2025.winlp-main.14",
pages = "65--74",
ISBN = "979-8-89176-351-7",
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 \textbf{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."
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<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.</abstract>
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%0 Conference Proceedings
%T ECCC: Edge Code Cloak Coder for Privacy Code Agent
%A He, Haoqi
%A Xu, Wenzhi
%A Liu, Ruoying
%A Tang, Jiarui
%A Li, Bairu
%A Lin, Xiaokai
%Y Zhang, Chen
%Y Allaway, Emily
%Y Shen, Hua
%Y Miculicich, Lesly
%Y Li, Yinqiao
%Y M’hamdi, Meryem
%Y Limkonchotiwat, Peerat
%Y Bai, Richard He
%Y T.y.s.s., Santosh
%Y Han, Sophia Simeng
%Y Thapa, Surendrabikram
%Y Rim, Wiem Ben
%S Proceedings of the 9th Widening NLP Workshop
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-351-7
%F he-etal-2025-eccc
%X 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.
%R 10.18653/v1/2025.winlp-main.14
%U https://aclanthology.org/2025.winlp-main.14/
%U https://doi.org/10.18653/v1/2025.winlp-main.14
%P 65-74
Markdown (Informal)
[ECCC: Edge Code Cloak Coder for Privacy Code Agent](https://aclanthology.org/2025.winlp-main.14/) (He et al., WiNLP 2025)
ACL
- Haoqi He, Wenzhi Xu, Ruoying Liu, Jiarui Tang, Bairu Li, and Xiaokai Lin. 2025. ECCC: Edge Code Cloak Coder for Privacy Code Agent. In Proceedings of the 9th Widening NLP Workshop, pages 65–74, Suzhou, China. Association for Computational Linguistics.