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        <title>Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models</title>
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    <abstract>Code pre-trained language models (CPLMs) have received great attention since they can benefit various tasks that facilitate software development and maintenance. However, CPLMs are trained on massive open-source code, raising concerns about potential data infringement. This paper launches the study of detecting unauthorized code use in CPLMs, i.e., Code Membership Inference (CMI) task. We design a framework Buzzer for different settings of CMI. Buzzer deploys several inference techniques, including signal extraction from pre-training tasks, hard-to-learn sample calibration and weighted inference, to identify code membership status accurately. Extensive experiments show that CMI can be achieved with high accuracy using Buzzer. Hence, Buzzer can serve as a CMI tool and help protect intellectual property rights. The implementation of Buzzer is available at: https://github.com/KDEGroup/Buzzer</abstract>
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    <identifier type="doi">10.18653/v1/2024.findings-emnlp.621</identifier>
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%0 Conference Proceedings
%T Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models
%A Zhang, Sheng
%A Li, Hui
%A Ji, Rongrong
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-code
%X Code pre-trained language models (CPLMs) have received great attention since they can benefit various tasks that facilitate software development and maintenance. However, CPLMs are trained on massive open-source code, raising concerns about potential data infringement. This paper launches the study of detecting unauthorized code use in CPLMs, i.e., Code Membership Inference (CMI) task. We design a framework Buzzer for different settings of CMI. Buzzer deploys several inference techniques, including signal extraction from pre-training tasks, hard-to-learn sample calibration and weighted inference, to identify code membership status accurately. Extensive experiments show that CMI can be achieved with high accuracy using Buzzer. Hence, Buzzer can serve as a CMI tool and help protect intellectual property rights. The implementation of Buzzer is available at: https://github.com/KDEGroup/Buzzer
%R 10.18653/v1/2024.findings-emnlp.621
%U https://aclanthology.org/2024.findings-emnlp.621/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.621
%P 10593-10603

Markdown (Informal)

[Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models](https://aclanthology.org/2024.findings-emnlp.621/) (Zhang et al., Findings 2024)

• Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models (Zhang et al., Findings 2024)

ACL

• Sheng Zhang, Hui Li, and Rongrong Ji. 2024. Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10593–10603, Miami, Florida, USA. Association for Computational Linguistics.