Printer fingerprinting techniques have long played a critical role in forensic applications, including the tracking of counterfeiters and the safeguarding of confidential information. The rise of 3D printing technology introduces significant risks to public safety, enabling individuals with internet access and consumer-grade 3D printers to produce untraceable firearms, counterfeit products, and more. This growing threat calls for a better mechanism to track the production of 3D-printed parts. Inspired by the success of fingerprinting on traditional 2D printers, we introduce SIDE (\textbf{S}ecure \textbf{I}nformation Embe\textbf{D}ding and \textbf{E}xtraction), a novel fingerprinting framework tailored for 3D printing. SIDE addresses the adversarial challenges of 3D print forensics by offering both secure information embedding and extraction. First, through novel coding-theoretic techniques, SIDE is both~\emph{break-resilient} and~\emph{loss-tolerant}, enabling fingerprint recovery even if the adversary breaks the print into fragments and conceals a portion of them. Second, SIDE further leverages Trusted Execution Environments (TEE) to secure the fingerprint embedding process.

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