Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models

• URL: http://arxiv.org/abs/2312.07200v2
• Date: Tue, 18 Feb 2025 03:50:53 GMT
• Title: Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models
• Authors: Sheng Zhang, Hui Li,
• Abstract summary: 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.
• Score: 6.507322607667119
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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.

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