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

• URL: http://arxiv.org/abs/2312.07200v1
• Date: Tue, 12 Dec 2023 12:07:54 GMT
• Title: Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models
• Authors: Sheng Zhang, Hui Li
• Abstract summary: This paper launches the first study of detecting unauthorized code use in CPLMs. We design a framework Buzzer for different settings of Code Membership Inference task.
• Score: 7.6875396255520405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 first 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 distilling the target CPLM, ensemble inference, and unimodal and bimodal calibration. 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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