A Causal Perspective on the Role of Training Dynamics for Interpreting Privacy Risks in Code Models
Causal analysis of code-LLM training dynamics reveals how personally identifiable information gets memorised, offering a principled lens for GDPR-relevant model auditing.
Summary written by editorial AI · Source link below
arXiv:2512.07814v3 Announce Type: replace-cross Abstract: Large language models for code (LLM4Code) have greatly improved developer productivity but also raise privacy concerns due to their reliance on open-source repositories containing abundant personally identifiable information (PII). Prior work shows that commercial models can reproduce sensitive PII, yet existing studies largely treat PII as a single category and overlook the heterogeneous risks among different types. We investigate wheth
Editorial Analysis
As enterprises adopt code-generation assistants, understanding the causal mechanisms behind PII memorisation is essential for GDPR-compliant deployment and data-protection impact assessments.
Incorporate causal training-dynamics audits into your AI model evaluation pipeline before deploying code-generation tools internally.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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