Code datasets are of immense value for training neural code completion models. Companies and organizations have made substantial investments to establish and process these datasets. Unluckily, these datasets, either built for proprietary or public usage, face the high risk of unauthorized exploits, resulting from data leakages, license violations, etc. Even worse, the black-box nature of neural models sets a high barrier for externals to audit their training datasets, which further connives these unauthorized usages. In this talk, I will share a framework and an imperceptible watermarking technique for protecting open-source code repositories and code datasets in general. The embedded watermark can trace the dataset usage in training neural code models with an effective validation method.